S / 01
Agent systems
Multi-step, tool-using agents that actually ship. Planning, memory, guardrails, evals — the boring parts done right.
- Tool-use
- Planning
- Memory
- Orchestration
- Guardrails
AI Engineering Studio · Booking 2026
We embed.We engineer.You own.
A senior AI engineering studio embedded with your team. The code, the models, the evals, yours from day one
Currently shipping for—Retail·Fintech·Logistics·Healthtech
02 — What we do
A full AI studio, shaped to your roadmap.
திண்ணைThinnai (n.) — the porch where ideas are thought out loud
We embed as your team. Scope a single sprint or an 18-month roadmap — senior engineers, ML researchers, and product designers plug into your stack from day one.
S / 02
RAG & retrieval
Grounded answers over your corpus. Hybrid search, re-ranking, citations, access control — tuned to your latency and cost budget.
- Hybrid search
- Chunking
- Re-ranking
- Citations
- ACL
S / 03
Custom ML models
When off-the-shelf models don't fit — classical ML, fine-tuning, distillation. From research spike to production endpoint.
- Fine-tuning
- Distillation
- Classical ML
- Evaluation
- Serving
S / 04
Data pipelines
Ingestion, cleaning, labeling, vectorization — the unglamorous scaffolding that determines whether anything above it works.
- ETL
- Labeling
- Vector stores
- Streaming
- Quality
S / 05
MLOps & infra
Observability, drift detection, cost monitoring, CI for models. We give you a spine that survives the second quarter.
- Observability
- Evals
- CI / CD
- Cost
- Drift
S / 06
LLM integration
Embed foundation models into real products. Prompt engineering, routing, caching, structured output — wired into your stack.
- Prompting
- Routing
- Caching
- Structured output
- Streaming
03 — How we work
From the porch to production, in five moves.
Week 0 — Discovery
We start where Tamil homes do — on the porch, in the open.
Two to three working sessions with your team. No decks, no NDAs-before-hello. We map the problem, name the unknowns, and agree on what “good” looks like — in plain language.
Problem briefStakeholder mapSuccess metrics
04 — Selected work
Shipped in the last twelve months.
Retail · SaaS2025
Support agent for a mid-market retailer
78%Auto-resolved
11sMedian reply
4.6CSAT / 5
Fintech · Compliance2025
RAG spine for a series-B fintech
2.3MDocs indexed
94%Citation acc.
6wkTo launch
Logistics · Enterprise2024
Forecasting MLOps overhaul
-34%Forecast error
9→1Deploy steps
24/7Drift monitor
05 — Why outsource
Senior AI craft, without the hiring cycle.
V / 01
Ship in weeks, not quarters
Your first demo is live within 15 working days. We don't do discovery theatre — we do the work.
V / 02
Senior by default
Every engineer on your project has shipped production AI before. No juniors learning on your dime.
V / 03
Scales up and down
Need two engineers for a sprint or eight for a quarter? Same team, same context, different shape.
V / 04
You own the IP
Code, models, evals — yours from commit zero. No vendor lock-in, no black boxes, no licensing games.
V / 05
Evals come first
We don't ship AI without a measurement framework. If we can't prove it works, we don't claim it does.
V / 06
One fixed rate
Flat monthly engagement. No line-item surprises, no “scope change” invoices buried in the PDF.
06 — In their words
Teams who keep us on speed-dial.
“They moved faster than our own team — and left us with code we could actually maintain. Rare combination.
Placeholder · VP Eng
Retail SaaS
“The eval harness alone paid for the engagement. We stopped guessing whether our agents were getting better.
Placeholder · CTO
Fintech
“Felt like we'd hired four senior engineers on a two-week start date. That's basically what happened.
Placeholder · Head of Data
Logistics
07 — Let's talk
Pull up a chair
on the thinnai.
Tell us the problem. We'll tell you what it'd take.
or write to [email protected]