AI Features That Earn Their Inference Bill
We build AI into products that already make money — RAG over your real documents, agents that close real tickets, classification on real customer data — and we measure it against the boring metric the CFO actually cares about: did the inference bill pay for itself this month? No demos that fall over on the second prompt, no GPT wrappers we charge enterprise prices for, no hallucinations in production we did not see coming.
You don't need another GPT demo.
You need an AI feature that pays for itself.
Most AI projects die for the same three reasons: the demo never survived real data, the inference bill grew faster than the revenue it generated, or nobody could tell whether it was actually working. We build the boring, measured kind — with evals, cost budgets, fallback paths, and a kill switch the COO can press at 3AM.
The demo wowed everyone, then died on real data
It worked on the three cherry-picked examples in the slide deck. Then it met your actual support tickets, your actual PDFs, your actual users typing in three languages — and the hallucination rate hit 40%.
The inference bill is eating the margin
You picked the biggest model for everything because nobody benchmarked the smaller ones. The feature works but it costs more per call than the customer is paying for the whole product.
Nobody can tell if it is working
No evals, no ground-truth set, no offline regression suite. Every prompt change is a vibe check. The model provider silently updates the endpoint and your output quality drops overnight and you only find out from a customer email.
What You Actually Get
No vague deliverables. Here's exactly what lands in your hands.
A feature shipped to real users
Not a notebook. Not a Streamlit demo. A production endpoint behind your auth, your rate limits, your observability. Wired into the product where the user already is.
An eval suite you can trust
A ground-truth set, an offline scoring pipeline, a regression dashboard. Every prompt or model change runs through it. No "I think it got better".
A cost model and a budget
Cost per inference, cost per user, monthly forecast, alert when you blow the budget. We tier the models — fast and cheap for the easy cases, expensive only when it earns it.
A kill switch and a fallback
Feature flag to turn the AI off without taking the product down. Deterministic fallback path for when the model provider has an outage. Your COO can press the button at 3AM.
A Real AI Engineering Team
Shipping AI is not "one prompt engineer with a ChatGPT account". Six roles you get on every Pillai Infotech AI build.
AI Solutions Architect
Maps the business problem to the right AI shape — RAG, agent, classifier, fine-tune, or "do not use AI for this". Picks the model tier that fits the cost envelope.
Senior AI / ML Engineer
Embeddings, vector stores, retrieval, reranking, tool use, function calling, structured output. Knows when to RAG and when to fine-tune and when to do neither.
Eval & Quality Engineer
Builds the ground-truth set, the offline eval pipeline, the regression dashboard, the LLM-as-judge harness. The reason you actually know whether it is working.
Backend & Integration Engineer
Wires the AI into your existing app — auth, rate limits, queues, retries, caching, fallback paths. Makes sure a 10s LLM call does not freeze the UI.
AI Safety & Compliance
Prompt injection defences, PII redaction, data residency, model usage policies, audit logs. Files the paperwork your legal and compliance teams need.
Cost & Performance Lead
Tracks inference cost per call, per user, per feature. Benchmarks smaller models against bigger ones. The engineer who makes the CFO smile.
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.
AI Features We Know How to Ship
We pick the AI shape to match the problem, not the buzzword on the conference stage.
📚 RAG over your real documents
Search and Q&A across your PDFs, contracts, knowledge base, support tickets. Real chunking strategy, hybrid retrieval, reranking, citations the user can click. Not "load the whole doc into the prompt".
🤖 Agents that close real tickets
Tool-using agents wired to your real APIs — refunds, lookups, ticket triage, escalation. Bounded scope, audit trail, human-in-the-loop on the dangerous actions.
🏷️ Classification & extraction at scale
Categorising tickets, extracting fields from invoices, tagging customer feedback, parsing emails. Where a small fine-tuned model often beats GPT-4 at a hundredth of the cost.
✍️ Drafting & assistive writing
Drafts of emails, summaries, reports, replies — for a human to review and send. We design the UX so the human always has the last word and the audit trail proves it.
🔍 Semantic search & recommendations
Embedding-based search, hybrid with keyword, personalised ranking. Often the highest-ROI AI feature in your product because it improves something users already do.
🎙️ Voice, transcription, multimodal
Whisper-class transcription, speaker diarisation, real-time voice agents, vision on documents and images. Where the model is finally good enough to ship.
The AI Stack We Use
We are not married to one provider. We pick the model that wins on cost and quality for your task — and we benchmark before we commit.
Models & Providers
Retrieval & Vectors
Frameworks & Tooling
Eval & Ops
A Six-Stage AI Delivery Process
Built around the reality that AI features earn or lose money on every single inference call.
Discovery & ROI Sizing
Which workflow, how many calls per month, what does each call replace, what is a wrong answer worth. If the maths does not work, we will tell you and not build it.
Build the Eval Set First
Before any prompt is written we collect 50–200 real examples with ground-truth answers. Every change later is scored against this set. No vibe checks.
Prototype & Model Bake-off
We benchmark three or four model tiers — from free open-source to flagship — against your eval set. You see the cost-quality trade-off in numbers, not opinions.
Productionise with Guardrails
Auth, rate limits, retries, fallback path, structured output validation, prompt-injection defences, PII redaction, full audit log. The boring 80% of shipping AI.
Launch with Cost & Quality Dashboards
Cost per call, eval pass rate, latency p50/p95, fallback rate, user thumbs-up rate. All on one dashboard. Alert when any of them drift.
Continuous Eval & Drift Watch
The model provider will silently change the endpoint. We re-run the eval weekly. You hear about quality drops from us, not from a customer email.
Three Ways to Engage
AI projects don't fit one shape. Pick the one that matches your stage.
AI Feasibility Sprint
Two-week engagement to pick the right AI shape, build the eval set, run a model bake-off, and produce a cost-quality report you can take to the board.
- Eval set + bake-off
- Cost-quality trade-off in writing
- Honest go / no-go recommendation
Fixed-Scope AI Build
End-to-end delivery from feasibility to a production AI feature wired into your app, with evals, dashboards, guardrails, and warranty.
- Fixed scope, fixed price
- Typical: 8–16 weeks
- 60-day post-launch warranty
Embedded AI Squad
A dedicated AI engineer + backend + eval lead working alongside your team on a continuous AI roadmap.
- AI eng + backend + eval + PM
- Monthly retainer, scale up/down
- Best for: ongoing AI feature pipeline
Honest Answers to AI Reality Questions
The questions every smart buyer asks before signing. Here's what we tell them.
Should we use OpenAI, Claude, Gemini, or open-source?
Depends on your task, your latency budget, your data residency rules, and your cost envelope. We benchmark three or four against your eval set before recommending one — and we will tell you when a free open-source model beats the flagship one for your specific job. We are not married to any provider.
Do we need to fine-tune a model?
Usually no. Start with prompt engineering, then RAG, then function calling, then small fine-tunes if structured tasks demand it, and only then full fine-tuning. Most projects never need step three. We will tell you honestly when fine-tuning earns its complexity.
How do you stop the model hallucinating?
Three layers. One: ground every answer in retrieved context with citations the user can click. Two: structured output validation — if the model returns garbage, we reject and retry. Three: an eval suite that fails the build when hallucination rate climbs. You cannot eliminate hallucinations, but you can measure and bound them.
How much will inference cost in production?
We model it in week one before writing code. Cost per call, calls per user per month, cost per active user, monthly forecast. We tier the models so cheap calls handle the easy 80% and expensive ones only fire when they earn it. If the maths does not work, we will tell you and not build it.
What about data privacy and model providers?
Your choice — we configure the stack around your constraints. Zero-retention APIs, EU data residency, on-prem models, BYO-cloud deployment, redaction of PII before it ever leaves your network. We have shipped AI for healthcare and fintech where the data never touches a hyperscaler.
How do you handle prompt injection?
Treat user input as untrusted by default. Strict tool whitelists, output validation, separation of system and user context, scoped credentials for any tool the agent calls, audit log on every action. We assume the attacker will try, and we test for it.
What does an "eval" actually look like?
A frozen set of 50–500 real input-output pairs with ground-truth answers, scored automatically — exact match, semantic similarity, LLM-as-judge, or task-specific metrics. Every prompt change runs through it before merge. The dashboard shows pass rate, regressions, and per-category breakdowns. If you do not have evals, you do not have an AI feature, you have a vibe.
Can the AI take real actions, or just suggest?
Both, with a dial. Read-only and suggest-only is the safe default. For write actions we wire in human-in-the-loop approval, scoped credentials, idempotency keys, and a full audit trail. Fully autonomous only on actions where the blast radius of a wrong call is small.
Will this still work in two years when the models change?
Yes, because we abstract the model behind a thin layer and we have evals. When a new model comes out we re-run the eval set, compare cost and quality, and switch if it wins. Your code does not care which provider is behind the wall.
Can you sign an NDA before we share details?
Always. NDA before the first call. Data, prompts, evals and model choices stay under your control. We are happy to work inside your tooling and your cloud account if that is what compliance requires.