Our Company
Jillani SofTech Enterprise AI Studio
We engineer production AI systems that deliver measurable ROI for enterprises across the USA, UK, EU and Australia. No demos. No proofs of concept. Live systems.
Jillani SofTech is an AI-first technology company founded by Muhammad Ghulam Jillani. We partner with enterprises in Banking, Healthcare, LegalTech, Retail, and Manufacturing to design, build, and scale intelligent systems - from RAG-powered knowledge platforms to autonomous multi-agent pipelines and n8n workflow automations. Every engagement begins with defined KPIs and ends with systems in production generating measurable business value.
27+Enterprise Clients · USA · UK · EU · Australia
22+AI Systems in Production · 99.9% Uptime
24/7Automated Monitoring · Alerting · Drift Detection
Generative AI & LLMs
Custom deployments on Claude Opus 5, GPT-5, Gemini 3 and Llama 4, grounded in your proprietary data, with evaluation gates so accuracy is measured, not assumed.
LangChain RAG & Agentic AI
Enterprise knowledge retrieval pipelines using LangChain, LangGraph, and vector databases with 61% better retrieval precision across millions of records.
n8n & Intelligent Automation
AI-powered business process automation connecting 400+ services, self-hosted for full data sovereignty, running 24/7 and reducing manual workloads by 65%.
Multi-Agent AI Systems
Advanced agent orchestration using CrewAI and LangGraph. Autonomous agents that collaborate, self-correct, and complete complex multi-step operations without human oversight.
Cloud AI & MLOps
Certified across AWS, Azure and GCP with scalable MLOps pipelines, automated CI/CD, drift monitoring and zero-downtime deployments. 29% faster deployment cycles.
Predictive Analytics & BI
Advanced forecasting and business intelligence on Snowflake and Databricks, turning raw enterprise data into real-time strategic intelligence leadership can act on confidently.
Industries We Serve
Domain Expertise Across Sectors
Production AI deployed for regulated, high-stakes enterprises where accuracy, compliance, and ROI are non-negotiable.
Banking & FinTech
Fraud detection, autonomous risk advisors, transaction monitoring, and document automation for financial institutions.
Healthcare & MedTech
HIPAA-compliant clinical decision support, patient monitoring, and medical document intelligence for hospital networks.
LegalTech
Intelligent contract analysis, compliance QA, and regulatory document review at 91% citation accuracy with zero hallucinated citations.
Retail & E-Commerce
GenAI shopping copilots, demand forecasting, RLHF-tuned recommendation engines, and inventory optimisation.
Manufacturing
Predictive maintenance, IoT analytics, and real-time computer-vision quality control across global facilities.
Enterprise SaaS
RAG knowledge platforms, internal copilots, and intelligent automation embedded into your existing stack.
How I Work
From Idea to Production ROI
A transparent, KPI-anchored process. You see working software every week - not just status reports.
01
Discovery & ROI Mapping
We define KPIs and success metrics before any code. You know the expected return upfront.
02
Architecture & Design
I design the data flow, model choices, and compliance approach tailored to your existing stack.
03
Build & Iterate
Weekly demos and full transparency. Working software every sprint, fully version-controlled.
04
Deploy to Production
Zero-downtime deployment across AWS / Azure / GCP with monitoring, CI/CD, and drift detection.
05
Support & Scale
Continuous monitoring and alerting, plus maintenance and tuning, so accuracy does not decay quietly after go-live.
Technical Depth
The Stack Behind Production AI
Generative AI & LLMs
Claude Opus 5 · GPT-5 · Gemini 3 · Llama 495%
RAG Systems · Agentic AI · LangGraph93%
Fine-Tuning · RLHF · LoRA · QLoRA90%
Vector DBs · Pinecone · Weaviate · Qdrant91%
Prompt Engineering · Chain-of-Thought94%
Cloud AI & MLOps
AWS · SageMaker · Bedrock · Lambda92%
Azure AI · OpenAI Service · Azure ML87%
GCP Vertex AI · BigQuery · Cloud Run84%
MLflow · ZenML · LangSmith · LLMOps88%
Docker · Terraform · CI/CD · GitHub Actions84%
Agentic AI & Automation
Python · FastAPI · Async Pipelines97%
n8n · Make.com · Zapier · Power Automate92%
CrewAI · AutoGen · LangGraph · LangSmith91%
Groq · Together AI · Replicate · HF Hub88%
TensorFlow · PyTorch · YOLO · OpenCV87%
Claude Opus 5Claude Sonnet 5Claude Haiku 4.5Claude Agent SDKClaude CodeMCPGPT-5Gemini 3Llama 4Mistral LargeDeepSeek V3Qwen 3LangGraphLangChainLangSmithLlamaIndexCrewAIAutoGenn8nMake.comZapierAWS BedrockAmazon QSageMakerAzure OpenAIVertex AI Agent BuilderGroq CloudTogether AIReplicateHugging Face HubPineconeWeaviateQdrantFAISSMLflowZenMLSnowflakeDatabricksFastAPIStreamlitvLLMOllamaWeights & BiasesKubeflowDockerNVIDIA CUDAUiPath
Engagement Models
3 Ways In, One Standard of Delivery
Scope and acceptance criteria are agreed before the build starts, so you are buying an outcome rather than hours. Most clients start with 1 narrow workflow and scale up only once it has earned that. Whichever path you take, the engagement ends the same way: a working system in your cloud, documentation your team can use, and code you own outright.
Scoping Call
30 minutes, free. The workflow worth tackling first, whether your data can support it, and a rough cost and timeline in writing afterwards. An honest no if AI is the wrong tool.
Free, no obligationRAG or Automation Build
2 to 4 weeks, from $3,500. One system solving 1 clear problem, live in production and measured against acceptance criteria set before the build starts. Not a proof of concept.
Fixed scope, confirmed in writingEnterprise AI Platform
8 to 16 weeks, $15K to $60K+. Multi-agent systems, APIs and dashboards with the MLOps, governance and HIPAA, GDPR or SOC 2 architecture around them, handed over documented.
Milestone billing you sign offRetainer and Team Augmentation
$4.8K to $6K per month. Dedicated engineering hours inside your team: monitoring, drift detection, model refresh and roadmap input, so the system keeps earning after go-live.
Rolling, 6 months or longerFrequently Asked Questions
Answers Before You Reach Out
The questions enterprise teams ask most often. Have another one? Just send a brief below.
3 engagement models, quoted per engagement after a free scoping call: a 2 to 4 week RAG or Automation Build from $3,500, an 8 to 16 week Enterprise AI Platform at $15K to $60K+, and a Retainer at $4.8K to $6K per month for teams that need ongoing capacity. Ranges move with data sensitivity, integration count and compliance load, then the quote is fixed in writing. Every quote is anchored to the ROI we define in discovery, so you know the expected return before committing.
Discovery wraps in days, not weeks. You'll typically see a working demo within the first 1-2 weeks, and most production systems ship in weeks rather than months - with weekly demos so there are never any surprises.
Yes. Compliance is designed into the architecture from day one, not bolted on later. I've shipped HIPAA-compliant clinical systems and GDPR/SOC 2-aligned compliance copilots for regulated enterprises across healthcare, finance, and legal.
Absolutely. NDAs are standard, and I offer self-hosted deployments (e.g. self-hosted n8n and private model endpoints) for full data sovereignty. Your proprietary data never leaves infrastructure you control unless you choose otherwise.
All 3 major clouds - AWS, Azure, and GCP - and I am certified on each, plus Claude Certified Architect with Anthropic for LLM system design. I'll recommend the platform that best fits your existing stack, budget, and compliance needs rather than forcing a one-size-fits-all approach.
Yes. Continuous automated monitoring, alerting and drift detection ship with the system, and maintenance and optimisation run on a retainer with a response window agreed in the contract. Production AI needs drift detection, model refreshes, and ongoing tuning to keep delivering ROI, and I keep your systems performing long after go-live.
Have an AI Project in Mind?
Let's define the ROI before you commit a single dollar. Book a free 30-minute strategy call and walk away with a clear plan - whether or not we work together.