Production AI, Not Demo AI
We don't ship Jupyter notebooks or ChatGPT wrappers. We build machine-learning, LLM, and computer-vision systems that survive Monday-morning traffic, hallucinate less, and earn back what they cost — with monitoring, evals, and a human in the loop on day one.
You don't need another AI demo.
You need a model that survives Monday.
Most AI projects die between the demo and the deployment. The notebook works, the slide deck wins the budget — and then nothing reaches the user. We build the boring infrastructure that makes AI actually ship: data pipelines, evals, monitoring, retraining, fallbacks. The exciting part is what your users see on the other side.
PoC graveyard
Three vendors, three demos, zero in production. Slick notebooks that crumble the moment real data, real users, or real edge cases show up.
Hallucinations & broken trust
LLM made something up in front of a customer. Now the whole project is on hold while legal asks questions you can't answer.
Data swamp, no foundation
Your data is messy, unlabeled, scattered across systems. Every vendor wants to skip past this — but the model is only as good as what you feed it.
What You Actually Get
No vague deliverables. Here's exactly what lands in your hands.
A model that runs in production
Versioned, containerized, deployed with proper rollback, sitting behind your auth — not a notebook on someone's laptop.
An evaluation harness
A reproducible test suite with ground-truth examples, accuracy/precision/recall metrics, and red-team prompts. You can audit every change.
MLOps pipeline
Data ingestion, retraining triggers, drift detection, automated CI/CD for models, cost monitoring per query. The plumbing AI vendors skip.
A human-in-the-loop UI
A review interface where your team approves edge cases, corrects mistakes, and feeds them back into the next training cycle.
A Real AI Engineering Team
AI in production needs more than a model. Six roles you get on every Pillai Infotech AI build.
Data Engineer
Cleans, labels, transforms, and pipelines your data. Without this, every model downstream is built on sand.
ML / LLM Engineer
Trains, fine-tunes, prompts, evaluates. Knows when to use a 7B open-source model, when to call GPT-4, and when classical ML wins.
MLOps Engineer
Builds the deploy pipeline, GPU scaling, version control for models, drift monitors, retraining schedules, cost dashboards.
AI QA & Eval Lead
Designs the eval set, runs regression tests on every model change, red-teams prompts for jailbreaks, tracks hallucination rate.
AI UX Designer
Designs the human-in-the-loop interface, the confidence indicators, the "I don't know" fallback flows, the trust signals.
Compliance & Safety Lead
Data residency, PII handling, model card documentation, prompt-injection defense, audit trails. Built in, not bolted on.
You See Everything. In Real Time.
Every Pillai Infotech project comes with a dedicated client dashboard. Kanban boards, live logs, test results, meeting notes — it's all visible the moment it happens. No status-report theatre, no "we'll get back to you", no surprises at the demo. You work with us like you work with your own team.
Kanban Board, Live
Every epic, every story, every task — visible on your dashboard. Drag, comment, reprioritize. It's the same board our team works from.
Documented Everything
Every decision, spec, API contract, and architecture diagram lives in the dashboard. Searchable, versioned, linked to the tasks they shaped.
Live Logs & Test Results
Build logs, deployment logs, test suite results — streamed to your dashboard the moment they run. You never have to ask "did the build pass?"
Meetings → Tasks, Automatically
Every meeting is recorded, transcribed, and every action point is auto-converted into a tracked task assigned to the right person. Nothing gets lost between calls.
Sprint Burndown & Velocity
See exactly how much work is done, how much remains, and our velocity over time. If a sprint is slipping, you see it the same moment we do.
Comment, Approve, Decide — In-Place
Comment on any task, approve designs, sign off on specs, and raise blockers directly in the dashboard. Everything tied to the work, not buried in email threads.
Use Cases That Pay for Themselves
We build AI where there's a measurable business outcome — not where it makes a good slide.
📄 Document AI
Invoice extraction, contract analysis, KYC, claims processing. 70-90% workload reduction is realistic.
📈 Forecasting & demand
Inventory, staffing, churn, revenue. Classical ML often beats LLMs here — and we'll tell you when.
🤖 Support automation
RAG-powered support agents grounded in your knowledge base, with safe handoff to humans.
🚨 Fraud & anomaly
Real-time detection on transactions, logs, or behavior with explainability built in.
👁️ Computer vision
Quality control, defect detection, OCR, surveillance, asset counting. Edge or cloud.
📚 RAG knowledge assistants
An AI that actually knows your company — your docs, contracts, policies, codebase. Cited, not hallucinated.
The AI Stack We Use
We pick the right model for the job. Open-source when it wins, frontier when it's needed.
LLMs
Vector & RAG
ML Frameworks
MLOps & Cloud
A Six-Stage AI Delivery Process
Built around the reality that most AI projects fail in stage 2 or stage 4. We don't skip them.
Discovery & Use-Case Framing
We sit with your team, map the workflow AI is meant to change, define success metrics in business terms (not F1 score), and surface the constraints — data, compliance, budget, latency.
Data Audit & Eval Set
Before any model: what data exists, what's clean, what needs labeling, what the ground-truth eval set looks like. No eval set, no green light.
Prototype + Eval
Smallest model that could possibly work, run against the eval set, measured honestly. We tell you if it doesn't clear the bar — early, in writing.
Productionize
Containerize, deploy behind your auth, instrument logging and cost metering, build the human-in-the-loop UI, integrate with your stack.
Monitor & Retrain
Drift detection, weekly eval re-runs, alerting on accuracy regression, scheduled retraining when new ground-truth data accumulates.
Optimize Cost
Smaller models where they win, caching, batching, distillation, prompt compression. AI that doesn't eat your margin.
Three Ways to Engage
AI projects don't fit one shape. Pick the one that matches your stage.
AI Discovery Sprint
Fixed 2-week sprint to validate whether AI is the right answer, what data you need, and what a realistic build looks like.
- Use-case framing & feasibility
- Data audit & gap analysis
- Honest go / no-go recommendation
Production AI Build
End-to-end build of a single AI capability — from data prep to production deploy with monitoring.
- Fixed-scope, fixed-price
- Typical: 8–16 weeks
- Includes 30-day production warranty
Embedded AI Team
A dedicated AI squad working alongside your team for ongoing AI development across multiple use cases.
- ML engineer + data engineer + MLOps
- Monthly retainer, scale up/down
- Best for: 3+ months of AI roadmap
Honest Answers to AI Reality Questions
The questions every smart buyer asks before signing. Here's what we tell them.
Will my data be used to train someone else's model?
No. We deploy in your cloud account or on-prem, with your API keys. When using third-party APIs (OpenAI, Anthropic, etc.) we use enterprise endpoints with zero-retention agreements. Data residency is part of the contract.
How do you stop the model from hallucinating?
You can\u2019t fully eliminate hallucinations from any LLM, but you can constrain them. We use grounded RAG, citation enforcement, output schemas, confidence thresholds, and human-in-the-loop fallbacks for low-confidence answers. Every project ships with a measured hallucination rate, not a hope.
Open-source model or OpenAI / Claude?
Depends. For privacy-sensitive workloads or high query volume, fine-tuned open-source (Llama, Mistral, DeepSeek) often wins on cost and control. For nuanced reasoning at low volume, frontier models are usually cheaper to operate. We benchmark both on your eval set before recommending.
My data is messy. Can you still build something?
Yes — and the data audit in week 1 tells us exactly how messy. Sometimes the answer is \u201cclean it first, then build,\u201d sometimes it\u2019s \u201cstart with classical ML on what\u2019s clean, expand later.\u201d We\u2019ll never pretend a model can paper over bad data.
How long until I see a working model?
Two to four weeks for a working prototype against a real eval set. Eight to sixteen weeks for a production-deployed model with monitoring. Compare to industry average of "we'll get back to you" — we'll show you Friday demos every week.
What does it cost to run in production?
We design for cost from day one and report it on the dashboard. Typical RAG system: $0.001-$0.05 per query depending on model. Custom fine-tuned model on your infra: pennies per thousand inferences. We tell you the unit economics before you commit.
Who owns the trained model and weights?
You do. 100%. Models, weights, training data, prompts, eval sets — all yours, in your repo, in your cloud account. No lock-in, no licensing games.
Can you integrate with our existing systems?
Yes — that's usually most of the work. We integrate with your SSO, your data warehouse, your ticketing/CRM/ERP, your existing alerting and observability. AI that lives outside the rest of your stack rarely gets used.
What if the model accuracy drops over time?
We monitor for drift. When metrics regress past the threshold, we get paged, retrain on new ground-truth, and ship a new version through the same eval pipeline. Optional retainer covers this end-to-end.
Can you sign an NDA before we share data?
Always. NDA before the first call. Data processing agreement before any data moves. You're in control of what we see.