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Upwork Top Rated Plus  ·  AWS · Azure · GCP Certified  ·  100% Job Success

Your AI Prototype Works in Demos. It Breaks in Production. We Fix That.

We design and build RAG systems, AI agents, and LLM SaaS products that hold up when real data hits. Every system ships with documented architecture, backend APIs, cloud deployment, and clean handoff. No demos. No excuses.

Book a Free Call See the Work
22+AI Systems Shipped
27+Enterprise Clients
100%Job Success Rate
$870KClient Costs Saved
3Cloud Certifications
24/7Production Support
AWS Certified
Azure Certified
GCP Certified
Upwork Top Rated Plus
100% Job Success
USA · UK · EU · Gulf
22+Production AI Systems
27+Enterprise Clients
100%Job Success Rate
47%Avg Efficiency Gain
$870KClient Costs Saved
24/7Production Support
Core Expertise

Six Things We Do Better Than Most

Not generalists. Not prompt engineers. We are AI engineers who have shipped production systems across six technical practice areas.

RAG Systems That Actually Retrieve

Most RAG systems fail because the retrieval layer is weak. We build with hybrid search (BM25 plus dense), cross-encoder reranking, citation grounding, and RAGAS evaluation before any system goes live. Your knowledge base, searchable in seconds.

LangChainLlamaIndexRAG FusionPineconeWeaviate

AI Agents That Execute Real Tasks

Not chatbots. Real agents that connect to your APIs, databases, and tools — then act. We build with LangGraph and CrewAI, with human approval gates, retry logic, and full trace observability so you always know what the agent did and why.

LangGraphCrewAIn8nAutoGen

Workflow Automation That Runs 24/7

We self-host n8n inside your infrastructure so your data never leaves. One platform. 400 integrations. AI decision logic baked into every workflow. No per-operation costs, no vendor lock-in, no downtime surprises.

n8nMake.comPower AutomateZapier

LLM Products With Real Backends

Full AI products from scratch. Backend APIs, authentication, multi-tenancy, usage tracking, dashboards, database design, and cloud deployment — all in one engagement. Ship an AI product in weeks, not quarters.

FastAPIReactPostgreSQLDockerStreamlit

MLOps Built for Regulated Environments

We run full-lifecycle ML infrastructure: training pipelines, model registries, shadow deployment, statistical drift detection, A/B testing, and CI/CD. Certified on AWS SageMaker, Azure ML, and GCP Vertex AI.

MLflowZenMLLangSmithKubernetesEvidently

Fine-Tuning With Ground-Truth Validation

We fine-tune domain-specific LLMs on your proprietary data using QLoRA and DPO alignment. Every model is validated against held-out benchmarks with ROUGE, BERTScore, and citation accuracy before it touches production traffic.

QLoRAPEFTDPORLHFGemmaLLaMA
Services

Everything You Need to Ship AI

From scoping through deployment. One partner. Full accountability. No vendor juggling.

RAG and Document Intelligence

Search, understand, and answer from PDFs, databases, CRMs, contracts, support tickets, and internal wikis. Hybrid retrieval, reranking, citation grounding, access control, and production evaluation pipelines included.

Hybrid SearchRerankingCitationsRBAC

AI Agents and Task Automation

Agents that write emails, review documents, qualify leads, check compliance, and run internal operations — connected to your CRM, databases, and APIs. Full audit trails. Human approval where it matters.

LangGraphCrewAIn8nAutoGen

LLM SaaS and Internal Copilots

We build the whole product. Backend APIs, user authentication, admin dashboards, usage analytics, database schema, and cloud deployment. Copilots for support, sales, HR, legal, and operations teams.

FastAPIReactPostgreSQLCloud

Machine Learning Systems

Forecasting, fraud detection, recommendation engines, and predictive analytics — with evaluation frameworks, drift monitoring, and retraining pipelines built in. Systems that improve over time, not stale models.

PyTorchXGBoostMLflowAWS Bedrock

Cloud AI and MLOps Infrastructure

Production-ready ML infrastructure on AWS, Azure, and GCP. Automated CI/CD, model registries, shadow deployment, drift alerts, and cost optimization. Triple-certified. Zero downtime deployments.

SageMakerAzure MLVertex AIKubernetes

AI Strategy and Architecture Review

Not sure whether to build or buy? Which LLM fits your use case? We map your workflow, design the architecture, model the ROI, and give you a phased plan you can act on. No fluff. Just a technical plan that works.

ArchitectureROI ModelingBuild vs Buy

Intelligent Process Automation

Combines RPA, AI, and NLP to automate complex workflows that rule-based bots cannot handle. Document processing, compliance checks, data extraction, and multi-system orchestration — without touching your live operations.

UiPathBlue PrismDocAINLP

LLM Fine-Tuning and Alignment

Domain-specific fine-tuning on your proprietary data using QLoRA, LoRA, and DPO alignment. Production evaluation pipelines with ROUGE, BERTScore, and human preference scoring. Models validated before deployment.

QLoRADPOPEFTRLHF

Data Engineering and Analytics

Data pipelines, warehouse integrations, and BI dashboards that feed your AI systems with clean, structured data. Snowflake, Databricks, BigQuery, and real-time streaming with Kafka and Airflow.

SnowflakeDatabricksPower BIAirflow
Intelligent Process Automation

Automation That Thinks, Not Just Clicks

Standard RPA follows rules. Our IPA layer combines AI reasoning, NLP, and computer vision to handle the messy, variable work that bots always break on.

01

End-to-End Process Automation

We map your highest-cost manual workflows, identify automation opportunities, and build AI-powered systems that eliminate the work entirely. Not just faster humans. No humans needed for these tasks at all.

02

IPA Managed Services

We run your automation infrastructure for you. 24/7 monitoring, proactive health checks, incident response in under two hours, and monthly optimization reviews. Your bots keep running while your team stays focused.

03

Team Augmentation

Need AI engineers embedded in your team? We place automation specialists, RPA developers, and AI architects directly into your delivery workflow. Enterprise-grade talent. No hiring cycle. No overhead.

04

Custom IPA Solutions

Built from scratch around your exact workflow constraints, tech stack, and compliance requirements. Includes full system integration, team training, documentation, and a 90-day transition roadmap so your team owns it.

Case Studies

10 Production Systems. Real Enterprise Clients.

Every number below is from a live production system. No projections. No demos. No made-up metrics.

LegalTechUSA and UKQLoRA · DPO · GCP Vertex AI

Domain-Specific LLM Fine-Tuning for Legal Intelligence

The Problem

General-purpose LLMs were hallucinating citations and producing inconsistent clause analysis across 11 jurisdictions. Every wrong answer created liability risk in client-facing workflows at a major international law firm.

What We Built

Fine-tuned Gemma 3 27B on 400,000 proprietary legal documents using QLoRA on GCP Vertex AI. DPO alignment sharpened precision for time-pressured legal professionals. A custom evaluation pipeline covering ROUGE-L, BERTScore, and citation accuracy ran before any model touched production.

400K Legal Docs Cases · Contracts 11 Jurisdictions QLoRA Fine-Tuning GCP Vertex AI DPO Alignment PEFT · Dataset Curation Hugging Face TRL Gemma 3 27B Legal Expert Model LangSmith Eval Pinecone · FastAPI 91% Accuracy Citation Precision Zero Hallucinations 60% Lower Inference Cost
91%Citation accuracy on legal queries
0Hallucinated citations post-alignment
27BParameter model on 400K proprietary docs
60%Lower inference cost vs proprietary APIs
"What distinguishes Jillani SofTech is their ability to translate complex regulatory requirements into fully operational AI systems. Most vendors talk about compliance. These engineers actually build for it."
Lisa Thompson, Chief Compliance Officer, Global Enterprises, UK
Financial ServicesUSAMLflow · AWS SageMaker · SOC 2

Enterprise LLMOps and Model Governance for a Regulated FinTech

The Problem

Five AI models running in production with no shared visibility, no drift detection, and no governance layer. Model behavior was tracked manually in spreadsheets. In a regulated financial environment, that is an audit failure waiting to happen.

What We Built

A unified LLMOps control plane across all five models: automated evaluations on a continuous schedule, statistical drift detection against rolling baselines, shadow deployment and A/B testing before any version touches live traffic, and a full model registry with SOC 2 compliant lineage and approval workflows.

Risk ScoringModel 1 Fraud DetectionModel 2 AML + AdvisoryModels 3-5 Governance Control Plane MLflow · ZenML · Evidently AI Shadow Deploy · A/B Testing AWS SageMaker · LangSmith Unified Dashboard SOC 2 Audit Logs Prometheus · Grafana Real-time Alerts Auto Promotion Data-driven rollout Safe rollback
5Production models unified under one platform
AutoDrift detection replacing manual monthly review
SOC 2Full audit-ready model lineage for compliance
3xFaster safe model promotion via shadow testing
"The systems are solid, the documentation is thorough, and the team remained accountable well past the delivery date. That combination of technical depth and post-launch ownership is genuinely rare at this level."
Evan Solomon, CEO, EFS Networks, USA
Enterprise SaaSUSA and UKn8n · LangGraph · GPT-5

Autonomous Revenue Execution and Pipeline Intelligence Platform

The Problem

14 disconnected sales tools. No coherent lead qualification. No CRM integrity. Revenue was slipping through gaps that no individual could monitor across multiple regions simultaneously.

What We Built

A 24/7 autonomous revenue layer on n8n and LangGraph. Real-time lead qualification, automatic CRM enrichment, personalized outreach across email and LinkedIn, predictive deal health scoring, and live pipeline reports delivered to leadership without any sales ops involvement.

Lead Sources Inbound · Outbound HubSpot · Salesforce 900+ triggers/day AI Agent Orchestration n8n self-hosted · LangGraph GPT-5 · ML Lead Scoring Outreach Sequencing Stripe · Slack · Gmail API Human approval gates Pipeline Intelligence Deal health scoring Live leadership reports Zero manual ops $340K Pipeline in 6 months 48% less manual work
48%Reduction in manual sales ops workload
1.8xIncrease in qualified lead throughput
$340KPipeline generated in first 6 months
900+Automated workflow triggers daily
"Their revenue execution platform created a scalable growth engine with pipeline intelligence we had been trying to build for two years. The results were immediate and measurable."
Michael Stevens, CTO, TechCorp Solutions, USA
HealthcareUSAAgentic RAG · GCP Vertex AI · HIPAA

HIPAA-Compliant Clinical Decision Intelligence for a Hospital Network

The Problem

A US hospital network had patient records, lab results, and ICD mappings sitting in silos. None of it was accessible at the point of care. Clinicians needed decision support without adding friction to already demanding workflows.

What We Built

A HIPAA-compliant clinical decision platform on GCP Vertex AI using a hybrid Agentic RAG pipeline. Patient records, lab data, and medical literature unified into one queryable layer. Role-based access controls across all clinical and admin roles. Full audit trail on every AI response. Zero-downtime SLA.

Patient RecordsEHR · FHIR Lab ResultsICD-10 · BigQuery Medical LiteratureWeaviate · RAGatouille Agentic RAG Pipeline LangGraph · Claude Opus 4 RBAC · LangSmith · Audit Log GCP Vertex AI · Docker Clinical Decision AI HIPAA Compliant Zero-downtime SLA Plain-language queries 91% Factual accuracy 48% less manual docs
91%Factual accuracy in clinical AI responses
27%Reduction in patient onboarding time
48%Decrease in manual clinical documentation
HIPAAFull compliance built into architecture
"The combination of factual accuracy, HIPAA compliance architecture, and real-time performance has had a direct and measurable impact on both patient care quality and operational throughput across our network."
Dr. Rachel Chen, Chief Medical Officer, HealthTech Innovations, USA
Enterprise SaaSUSA and UKLangGraph · RAG Fusion · Neo4j

Enterprise Knowledge Intelligence and Search Platform

The Problem

Critical institutional knowledge scattered across hundreds of documents and disconnected systems. Single-vector-search architectures had already failed. The organization needed multi-document, multi-hop reasoning at scale.

What We Built

Multi-agent reasoning with Neo4j knowledge graph integration for complex cross-document queries. RAG Fusion improved precision over single-retrieval approaches. Role-based access controls with complete audit trail across all departments. 8,000 queries per day at launch.

Knowledge Sources PDFs · Wikis · CRM ERPs · Slack · Email AWS S3 · Databases 8K+ queries/day Retrieval Layer RAG Fusion · Pinecone Neo4j Knowledge Graph Cross-encoder Reranking LangGraph Multi-agent RBAC · Full Audit Trail Cited Answers GPT-4o · Azure OpenAI Source citations Streamlit UI Plain language 54% Faster knowledge search 61% better precision 37% fewer tickets
54%Faster knowledge retrieval
61%Better retrieval precision vs prior tooling
37%Fewer support tickets from knowledge gaps
8K+Daily queries handled across departments
"They think about AI the way a senior technology architect thinks about enterprise systems. Not tools to add on top, but infrastructure to build around."
Frank Shines, Head of AI and Digital Transformation, USA
E-CommerceUSA and UKn8n · Claude Sonnet · GPT-5

24/7 Autonomous Customer Support and Brand Intelligence Platform

The Problem

A fast-scaling US e-commerce brand had support infrastructure falling apart under volume. Response times were degrading. Agents were overwhelmed. Brand content across five social platforms was inconsistent in tone and quality. Headcount was not the answer.

What We Built

A fully autonomous support platform across Instagram, TikTok, Facebook, LinkedIn, X, chat, and email. Claude Sonnet handles incoming queries using knowledge-grounded reasoning — managing refunds, routing tickets, and resolving issues without escalation in the majority of cases. A sentiment monitoring layer escalates only what requires human judgment.

Customer Channels Instagram · TikTok Facebook · LinkedIn X · Chat · Email 24/7 inbound Graph APIs · Webhooks AI Support Brain n8n · Claude Sonnet 4.5 GPT-5 · Pinecone RAG Sentiment Monitoring LangChain · PostgreSQL AWS Lambda · Docker Resolution Layer Auto-resolve or escalate Refunds · Routing · FAQs Brand tone enforced 61% Resolved without humans 44% faster response
61%Queries resolved without human escalation
44%Reduction in average customer response time
23%Improvement in audience engagement rate
24/7Global coverage with zero headcount increase
"Customer volume grew substantially post-launch while headcount remained flat. The AI handles what would have taken three full-time agents. It has completely changed our support economics."
VP of Customer Experience, US E-Commerce Brand
RegTechGermany and EUAgentic RAG · Azure OpenAI · LLMOps

Autonomous Regulatory Intelligence and Compliance Governance Platform

The Problem

A major European enterprise managing GDPR, EU CSRD sustainability mandates, and internal policy review across 8 countries simultaneously. Each compliance cycle required external legal consultants and months of manual effort.

What We Built

A regulatory intelligence platform that monitors regulatory feeds across all relevant frameworks, analyzes internal documents for compliance gaps in real time, generates risk flags with structured remediation guidance, and produces board-ready compliance reports in English, German, and French on demand. LLMOps governance layer makes every AI decision auditable.

Regulatory Feeds GDPR · CSRD · EU AI Act Internal Policies 8 Country Feeds Azure Blob Storage Compliance AI Engine LangChain · GPT-4o Azure ChromaDB · FastAPI MLflow · LangSmith Streamlit Dashboard Docker · CI/CD Board-Ready Reports EN · DE · FR Risk flags + remediation Auditable decisions Pinecone · Grafana 84% Gap classification 52% less audit work 2.3x faster reporting
84%Accuracy in automated compliance gap classification
52%Reduction in manual ESG auditing effort per cycle
2.3xFaster regulatory reporting across all jurisdictions
3Languages: English, German and French reports
"Their compliance platform reduced our risk exposure while improving audit readiness across multiple jurisdictions. Most vendors talk about compliance. These engineers actually build for it."
Chief Compliance Officer, Global Enterprise, UK
DevOps and EngineeringUSALangGraph · AutoGen · GPT-4o

Autonomous Software Delivery and Engineering Operations Platform

The Problem

An engineering organization spending more capacity managing their delivery pipeline than shipping product. Debugging was reactive, failure patterns repeated across sprints, and incident postmortems were inconsistent when they happened at all.

What We Built

A multi-agent engineering intelligence platform that integrates into the existing DevOps toolchain. It reviews pull requests before merge, diagnoses pipeline failures with specific remediation steps, generates validated code patches and test cases, monitors deployments for anomalies, and produces structured incident summaries after every significant event.

DevOps Signals GitHub · Jenkins Pull Requests Pipeline Failures AWS CloudWatch Multi-Agent CI/CD Brain GPT-4o · LangGraph AutoGen · LangChain Code review · Test gen LangSmith · MLflow PgVector · FastAPI Automated Actions PR comments + fixes Incident summaries Deploy anomaly alerts Kubernetes · Docker 38% Less debug time 29% faster deploys Fewer regressions
38%Reduction in debugging and incident resolution time
29%Faster deployment cycles across all environments
FewerProduction regressions and escaped defects per sprint
LowerManual DevOps intervention required per delivery cycle
"The autonomous software delivery platform changed how our engineering organization operates. Any engineering organization focused on sustained delivery velocity without sacrificing quality should be talking to this team."
Daniel Foster, Director of Engineering, NexaScale Systems, USA
Global EnterpriseUSA and EuropeGPT-4o · LangGraph · Neo4j

Enterprise Program Governance and Delivery Intelligence Platform

The Problem

A global enterprise running complex programs across multiple regions had no real-time visibility into execution. Status updates were manual summaries from people with a stake in how they read. Risks surfaced only after they had escalated. Cross-team dependencies tracked in spreadsheets that were stale before leadership reviewed them.

What We Built

An AI-powered program management layer that ingests live communication from Slack, email, and ticketing systems. It tracks timelines and blockers as they develop, generates predictive risk flags before they escalate, and produces clean executive briefings on demand. A persistent decision memory layer preserves institutional context across leadership transitions.

Live Data Feeds Slack · Email Jira · Tickets Multi-region programs Cross-team dependencies AI Delivery Intelligence GPT-4o · LangGraph Neo4j Knowledge Graph Pinecone · Azure OpenAI LangSmith · FastAPI Decision memory layer Executive Intelligence Predictive risk flags On-demand briefings Live blocker tracking Docker · PostgreSQL 28% Better on-time delivery Earlier risk detection Less manual reporting
28%Improvement in on-time project delivery
EarlierRisk identification across multi-team delivery
ReducedManual overhead in status reporting
LiveExecutive visibility across all active pipelines
"The program governance platform gave our executive team real-time visibility into risks, dependencies, and execution gaps before they became problems. It operates less like a reporting tool and more like an intelligent operations layer."
Isabella Muller, VP Strategy and Operations, EuroCore Group, Germany
Retail and E-CommerceUSAAWS Bedrock · RLHF · Snowflake

AI Personalization and Demand Forecasting Platform for Enterprise Retail

The Problem

Conversion rates were flat because the customer experience was generic across all segments. Inventory costs were climbing because demand planning was reactive and manual. Two problems compounding each other, neither being solved by the existing tools.

What We Built

A dual-layer AI platform: the personalization layer generates real-time product recommendations based on live behavioral signals, improving through reinforcement learning feedback loops. The supply chain layer predicts demand shifts and adjusts inventory planning before overstock or stockout conditions develop. Both operate through AWS Bedrock at sub-100ms response times.

Customer Layer Behavior signals Session data · Clicks Supply Chain Layer Inventory · Sales history Snowflake · Airflow Dual-Layer AI Platform AWS Bedrock · RLHF LangChain · Scikit-learn PostgreSQL · pgvector MLflow · FastAPI Sub-100ms response Personalization Engine Real-time recommendations 14% conversion uplift Demand Forecasting 62% better accuracy 31% inventory improvement 14% Conversion increase 62% Better forecasting
14%Increase in e-commerce conversion rate
31%Improvement in inventory planning accuracy
62%Improvement in demand forecasting accuracy
100msSub-100ms recommendation response time
"Two problems we had been fighting for three years, solved in one platform. The personalization numbers spoke for themselves within the first 30 days. Inventory planning changed how our buying team works."
VP of Digital, Enterprise Retail Group, USA
Jillani SofTech Engineering Stack
LLM OrchestrationLangChain · LangGraph · LlamaIndex · CrewAI · AutoGen
Production
Vector and Knowledge LayerPinecone · Weaviate · pgvector · FAISS · Neo4j · Qdrant
Active
Automation Enginen8n self-hosted · CrewAI · Make.com · Power Automate
Active
Cloud InfrastructureAWS SageMaker · Azure ML · GCP Vertex AI · Bedrock
Certified
Observability StackMLflow · LangSmith · Grafana · Prometheus · Evidently AI
24/7
How We Work

No Demos. No Notebooks.
Only Working Systems.

One Partner, Full Accountability
Architecture through deployment through support. No vendor coordination. No accountability gaps. You have one person to call.
Production-Ready from the First Sprint
Every system is tested against defined success criteria before it goes live. Clean architecture, documented handoff, and real monitoring from day one.
KPIs Before Code
We define what success looks like before we write a single line. Efficiency gains, cost reductions, retrieval accuracy. We track them throughout.
You See Progress Every Week
Working demos from sprint two. Structured updates. Honest communication about blockers. No surprises at handoff.
Outcomes

Numbers from Live Production Systems

Not projections. Not theoretical benchmarks. These come from deployed systems with real clients.

85%
Average Process Efficiency Gain
Across all automation deployments
65%
Reduction in Manual Workload
Via AI agents and workflow automation
91%
Best-in-class RAG Accuracy
Healthcare and legal systems
$870K
Client Costs Saved and Documented
Across all engagements
100%
Job Success Rate on Upwork
Every client satisfied. Every project delivered.
3-6mo
Typical ROI Payback Period
Most enterprise engagements
Industries

We Know Your Industry's Constraints

Every sector has its own regulatory requirements, data architecture, and operational reality. We build for those specifics, not generic templates.

🏦

Banking and Finance

Fraud detection, credit risk scoring, compliance automation, AML monitoring, and AI advisory platforms built for FSA, SEC, MiFID II, and Basel III with full auditability.

🏥

Healthcare

HIPAA-compliant clinical AI, medical documentation automation, diagnostic support, and hospital operations systems under zero-downtime SLAs.

⚖️

Legal and Compliance

Contract analysis, regulatory monitoring, e-discovery acceleration, and GDPR plus CSRD compliance systems for law firms and in-house legal teams.

🛒

Retail and E-Commerce

Demand forecasting, personalized recommendation engines, dynamic pricing, autonomous customer support, and inventory intelligence at scale.

💼

SaaS and Technology

Internal AI copilots, developer productivity tools, customer success AI, multi-tenant LLM SaaS products, and AI-powered feature development for tech companies.

🏭

Manufacturing and Supply Chain

Predictive maintenance AI, computer vision quality control, supply chain optimization, and production scheduling automation for operational excellence programs.

🏘️

Real Estate

Property valuation, market forecasting, lead qualification, document automation, tenant support, and portfolio insights for residential and commercial real estate teams.

👥

Human Resources

Resume screening, candidate matching, hiring workflows, workforce analytics, employee engagement tracking, and attrition prediction for growing HR teams.

Client Feedback

What Clients Say After We Deliver

Direct quotes from the people we built for. Every testimonial is tied to a real delivery.

"

Their revenue execution platform created a scalable growth engine with pipeline intelligence we had been trying to build for two years. The results were immediate and measurable. I would not hesitate to recommend them to any enterprise AI initiative.

MS
Michael StevensCTO, TechCorp Solutions, USA
Verified
"

The clinical intelligence platform operates at a level we would expect from top-tier enterprise vendors. Factual accuracy, HIPAA compliance, and real-time performance. It had a direct and measurable impact on both patient care quality and operational throughput across our network.

RC
Dr. Rachel ChenChief Medical Officer, HealthTech Innovations, USA
Verified
"

Their compliance platform reduced our risk exposure while improving audit readiness across multiple jurisdictions. Most vendors talk about compliance. These engineers actually build for it.

LT
Lisa ThompsonChief Compliance Officer, Global Enterprises, UK
Verified
"

The systems are solid, the documentation is thorough, and the team remained accountable well past the delivery date. That combination of technical depth and post-launch ownership is genuinely rare at this level of AI engineering.

ES
Evan SolomonCEO, EFS Networks, USA
Verified
"

The autonomous software delivery platform changed how our engineering organization operates. Pipeline failures, debugging cycles, and incident resolution now happen at a speed and consistency that was not achievable before.

DF
Daniel FosterDirector of Engineering, NexaScale Systems, USA
Verified
"

The program governance platform gave our executive team something we had been missing across every large initiative: real-time visibility into risks, dependencies, and execution gaps before they became problems.

IM
Isabella MullerVP Strategy and Operations, EuroCore Group, Germany
Verified
"

Their AWS-integrated AI data science platform reduced our model deployment time by 65% and improved prediction accuracy by 38%. The scalable architecture they designed is now the backbone of our entire analytics operation.

SC
Sarah ChenVP of Engineering, TechVentures Global, Germany
Verified
"

Their n8n lead management automation dropped our lead response time from 4 hours to under 3 minutes. Their n8n expertise is on another level. They designed workflows our internal team would never have conceived of independently.

MR
Marcus ReidCOO, GrowthEdge Partners, Canada
Verified
"

Our legal contract AI review platform built in six weeks. What took paralegals two full days now takes the AI two minutes at 92% accuracy. Their NLP expertise and pace of delivery are a rare combination in this market.

HM
Hannah MorrisonDirector of Innovation, LegalEdge UK, UK
Verified
"

Their AI talent acquisition system reduced our time-to-hire by 52% and improved candidate quality scores by 34%. The change management support they provided made rollout across 15 offices completely seamless.

PB
Priya BhatiaCHRO, NexGen Workforce Solutions, Germany
Verified
"

Our entire predictive real estate valuation engine built with Jillani SofTech. Model accuracy surpassed every commercial provider we had evaluated. Their ability to deliver enterprise AI at startup speed is remarkable. They are our exclusive AI partner going forward.

DK
Daniel KowalskiCEO, PropIntel Platform, EU
Verified
"

They think about AI the way a senior technology architect thinks about enterprise systems. Not tools to add on top, but infrastructure to build around. The knowledge platform gave us real-time visibility across complex multi-region initiatives in a way nothing prior had delivered.

FS
Frank ShinesHead of AI and Digital Transformation, USA
Verified
Technology

The Full Stack Behind Every Delivery

We pick the right tool for your use case, not the most familiar one. Here is every tool we work with across all practice areas.

Large Language Models
OpenAI GPT-5GPT-4oClaude Opus 4Claude Sonnet 4.5Gemini 2.5 ProLLaMA 4MistralDeepSeek v3Hugging Face
AI Frameworks and Orchestration
LangChainLangGraphLlamaIndexCrewAIAutoGenPhiDataDSPyLangSmith
RAG and Retrieval
RAG FusionAgentic RAGRAGatouilleHybrid Search BM25Cross-Encoder RerankingMetadata FilteringCitation GroundingRAGAS Evaluation
Vector Databases
PineconeWeaviateChromaDBQdrantFAISSpgvectorMilvusRedis VectorElasticsearchNeo4j Knowledge Graph
Workflow Automation and RPA
n8n self-hostedMake.comZapierPower AutomateUiPathAutomation AnywhereBlue PrismRetoolBubble.io
Cloud Platforms
AWS SageMakerAWS BedrockAWS LambdaAWS EC2Azure MLAzure OpenAIGCP Vertex AIBigQuery MLHerokuVercel
MLOps and LLMOps
MLflowZenMLLangSmithEvidently AIWeights and BiasesPrometheusGrafanaCI/CD Pipelines
Fine-Tuning and Alignment
QLoRALoRAPEFTRLHFDPO AlignmentHugging Face TRLGemma 3 27BLLaMA 4 Fine-tune
Machine Learning Frameworks
PyTorchTensorFlowKerasScikit-learnXGBoostLightGBMOpenCVspaCyYOLOONNX
Data Engineering and Analytics
Apache SparkApache KafkaApache AirflowSnowflakeDatabricksdbtTableauPower BIBigQueryPySpark
Backend, Deployment and DevOps
PythonFastAPIFlaskStreamlitReactNext.jsDockerKubernetesGitHub ActionsJenkinsTerraform
Databases
PostgreSQLMongoDBMySQLRedisNeo4jOpenSearchRedshift
Why Jillani SofTech

Six Reasons Enterprises Keep Coming Back

The things that separate production AI from prototype AI.

01

22 Production Systems Shipped

Real AI systems in real enterprises. Healthcare, finance, legal, retail, DevOps. Every engagement has documented outcomes. No prototypes. No demos that never went live.

02

Triple Cloud Certified

AWS, Azure, and GCP certified. We architect on any major cloud platform with enterprise-grade security and full compliance with your regulatory environment from day one.

03

100% Upwork Job Success

Top Rated Plus. The highest tier on Upwork. Perfect success rate across every single engagement. Every client satisfied. Every commitment honored. No exceptions.

04

Full Stack. One Partner.

Retrieval architecture, agents, APIs, databases, deployment, and monitoring. All under one engagement. One person accountable. No vendor coordination. No finger-pointing.

05

Compliance from the First Design Decision

HIPAA, GDPR, SOC 2, and CSRD built into the architecture before a single line of code is written. Not retrofitted at the end. Not added to a checklist after launch.

06

Accountable After Launch

Monitoring, SLAs, model performance tracking, and quarterly improvement cycles are part of every engagement. We do not build and disappear. We stay.

About Jillani SofTech

Built by an Engineer.
Run Like a Product Company.

I am Muhammad Ghulam Jillani, a Full Stack AI Engineer, Lead AI Data Scientist, and the founder of Jillani SofTech. I started this company because I kept seeing the same problem: enterprises wanted AI, but vendors kept delivering prototypes that broke the moment real data hit them.

So I built Jillani SofTech around one rule: nothing ships unless it works in production. Every system we deliver has documented architecture, real monitoring, clean handoff, and post-launch support. Not as an add-on. As a standard.

Five years and 22 production AI systems later, I still personally lead the architecture and delivery on every engagement. When you work with Jillani SofTech, you work with me directly, not a project manager passing notes between you and a team you have never met.

We are Top Rated Plus on Upwork with a 100% job success rate, triple-certified across AWS, Azure, and GCP, and recognized as a 24x LinkedIn Top Voice in AI. More importantly, we have 27 enterprise clients who came back for a second engagement, which is the only metric that actually matters.

If you have a workflow that is costing you time and money, data that is not being used, or an AI system that is not performing the way it should, book a call. I will tell you in 30 minutes whether we can fix it and what that looks like.

Book a Call with Muhammad Email Directly
22+Production AI Systems
100%Job Success Rate
27+Enterprise Clients
24/7Production Support
5+Years Engineering AI
24xLinkedIn Top Voice in AI

Your AI System Should Work. Not Just Demo Well.

Book a free 30-minute call. We will listen to your use case, ask the technical questions that matter, and give you an honest assessment of what is realistic, how long it takes, and what it costs.

Book Free Consultation Chat on WhatsApp
FAQ

Straight Answers to Common Questions

The questions we hear most often before an engagement begins.

What do you actually build?

RAG systems, AI agents, LLM SaaS products, MLOps pipelines, and production ML infrastructure. Every engagement covers the full stack: architecture, development, deployment, documentation, and post-launch support. We do not deliver notebooks or prototypes. Only working production systems.

How long does a project take?

RAG systems or n8n automation workflows typically take 2 to 4 weeks. Full enterprise AI platforms, multi-agent systems, or complete MLOps pipelines take 8 to 16 weeks. We use agile delivery with working demos from sprint two onward, so you see real progress throughout.

What does it cost?

A RAG chatbot for internal documents starts from around $3,500. A full enterprise AI platform with agents, APIs, dashboards, and cloud deployment typically ranges from $15,000 to $60,000 or more. We provide a detailed estimate after a free 30-minute scoping call.

Do you work with healthcare and financial services?

Yes. We have built HIPAA-compliant clinical AI for a US hospital network, SOC 2 ready financial model governance platforms, and GDPR plus CSRD compliance systems for EU enterprises. Security and compliance are built into the architecture from the first design decision.

What makes a RAG system fail?

Almost every RAG failure comes from weak retrieval, not a weak LLM. Poor chunking strategy, single-vector search without reranking, and no evaluation framework. We build with hybrid search (BM25 plus dense), cross-encoder reranking, citation grounding, and RAGAS evaluation validated against ground-truth benchmarks before deployment.

Do you support the system after delivery?

Yes. All engagements include post-launch monitoring, rapid incident response, and regular performance reviews. For ongoing clients, we offer structured monthly support retainers covering model updates, retrieval tuning, and system improvements. We stay accountable for what we build.

How do I get started?

Book a free 30-minute call at calendly.com/jillanisofttech/30mins, email m.g.jillani@jillanisoftech.com, or message directly on WhatsApp at +1-501-420-2439. In the first call, I listen to your use case, ask clarifying questions, and give you an honest assessment of what is feasible, how long it takes, and what it costs.

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Let's Talk About Your Problem

Share your use case and we will tell you in 30 minutes what the right approach looks like.

What We Help With

RAG and Document Intelligence

Your documents searched in seconds with citations and access control

AI Agents and Task Automation

Agents that connect to your tools and actually execute business tasks

LLM SaaS and Copilots

Full AI products: APIs, auth, dashboards, database, cloud deploy

MLOps and Cloud AI

Production pipelines, drift detection, model governance on any cloud

Compliance-Ready Architecture

HIPAA, GDPR, SOC 2 built in from day one, not retrofitted

Reach Us Directly

Phone USA 🇺🇸

+1-501-420-2439

Phone Pakistan 🇵🇰

+92-321-1174167

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Email

m.g.jillani@jillanisoftech.com

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