The first call is free · 45 minutes · no obligation
30+ CLIENTS · BUILDING SINCE 2016 · BENGALURU · CHANDIGARH · DUBAI · CAMBRIDGE · CALIFORNIA
Where builds stall
01Ungrounded in your own data
02No evaluation before ship
03No governance once it's live
04No ownership path after launch
03 · WHAT WE BUILD
Generation in production is not one prompt.
It's ten systems wired together — from what a model is grounded in, to what stops it before a bad output ships. Here is the full chain.
Ground
01
Retrieval-grounded generation (RAG)
Indexed, chunked, cited to source.
02
LLM fine-tuning & domain adaptation
Tuned on your data and terminology.
06
Synthetic data generation
Fills gaps without exposing real records.
Generate
03
Enterprise content generation
Copy, campaigns and documentation at scale.
04
Code generation & developer tooling
Wired into your existing review flow.
05
Image, video & multi-modal generation
Brand-consistent across media types.
07
Document & structured-output generation
Parses cleanly into downstream systems.
Orchestrate
08
Prompt engineering & orchestration
Chains models, tools and retrieval into one system.
Verify
09
Evaluation & quality harness
Scores every output before it ships.
10
Governance, brand-safety & IP control
What a model can say, and proof of it.
04 · MODEL LAYERS
Order the spend: prompting and retrieval before fine-tuning, applied in cost order.
Turn a general model into one that produces your work
Most generative AI budget goes to the wrong layer first. We apply four layers in cost order, so spend lands where it actually changes output.
01
Grounding before training
Retrieval and context grounding connect the model to your data before any training run — the cheapest lever, applied first.
02
Fine-tune only when it pays
We fine-tune only what grounding can't fix — tone, structure, domain reasoning that prompting alone won't hold.
03
Your voice, enforced
Style and terminology rules are enforced at the output layer, not left to chance in the prompt.
04
Checked, then shipped
Every output passes an evaluation gate — factuality, tone, safety — before it reaches a user.
Cheapest, applied firstMost expensive, applied last
05 · Prototype to production
A demo answers the question once. Production answers it a thousand times a day.
Every generative AI demo looks finished. What it never shows is what happens after: the same output, on-brand, cost-tracked and logged — every time, at volume. The model is one component. The engineering around it is the rest.
The demo
Looks right once, on a screen you control
No cost per generation, no ceiling on spend
No record of what it said or why
Runs on a laptop, not your stack
What we build
Cost that tracks value. Every generation priced and capped against what it's worth, not left open-ended.
Observability on every generation. Inputs, outputs and confidence logged — visible before a customer ever sees one.
Wired into your stack. Reads and writes through your existing systems, not a demo endpoint.
A record you can audit. Every output traceable back to the prompt, the data and the model version that produced it.
06 · WHY BUILDS STALL
Why most generative-AI builds stall before production
The model is almost never the problem. Name these on the first call, not the third.
01
Hallucination treated as a model flaw
Teams wait on a better model instead of grounding the one they have — retrieval and context checked before an answer ships.
02
No way to measure output quality
Without a scoring harness, “does this look right” is an opinion in a demo — one that stops holding once volume goes up.
03
Governance bolted on last
Guardrails added after the model is already generating for users mean rebuilding trust app-wide — which is what slips the date.
04
Generation economics ignored
Token cost and retries scale with usage, not with the demo. What works at ten requests a day fails on the invoice at ten thousand.
2 weeks, fixed price, yours to keep whether or not you build with us after.
STEP 2
Build
Evaluation and governance ship as deliverables in the build, not afterthoughts. No lock-in.
STEP 3
Production & continuous improvement
Logging, cost monitoring, and live-case tracking, running from day one.
DISCOVERY SPRINT
Find out which of your outputs a model should generate.
Most generative AI builds stall for the same handful of reasons — ungrounded facts, no evaluation gate, no owner once the pilot ends. Bring the workflow that's stalling. We'll name the reason on the first call.
01
02
06
03
04
05
07
08
09
10
01
02
03
04
