01
Stuck in pilot purgatory
The demo impressed everyone eight months ago. It's still a demo — because nobody designed for accuracy verification, error handling, or the compliance review that was always coming.
Services
AI development services
Most AI projects die between the demo and the deployment — blocked by compliance, unverifiable accuracy, or costs nobody modeled. We build LLM systems designed for the review that kills the others: validated outputs, humans in the loop, and an audit trail your compliance officer can actually review.
Who this is for
You’ll recognize the situation
02
Your data can't leave the building
Legal has ruled: no customer data in third-party training sets, no PHI outside covered infrastructure. Every vendor pitch you've seen quietly fails that test.
03
Nobody can say if it's working
The model 'seems good' but there's no labeled baseline, no accuracy number, and no way to spot drift — so every executive review reopens the question of whether to kill it.
What’s included
AI development, itemized
LLM application development
Document processing, extraction, and workflow automation built on strict schemas — every output validated against type and plausibility rules before it touches your systems.
Retrieval-augmented generation
RAG over your own corpus with source-grounded answers — every claim carries a pointer back to the document it came from, so users can verify instead of trust.
Human-in-the-loop workflows
Review queues where people confirm high-stakes outputs in one glance — confidence scores routing attention to where the model is least sure.
Evaluation and guardrails
Hand-labeled baselines, per-field accuracy tracking, and monthly drift audits — so quality shows up in a report before it shows up in production.
Private and compliant deployment
Models running inside your boundary or under BAA-covered infrastructure, PHI and PII excluded from logs, prompts version-pinned and change-controlled.
Cost and latency engineering
Model routing, caching, and batch strategies that keep unit economics predictable — you'll know the cost per document before you scale, not after.
How it runs
Four phases, no surprises
01
Diagnose
Review representative documents and the workflow, then build an agreed evaluation set. Use measured performance to decide whether a larger build is justified.
02
Plan
An architecture your compliance team reviews before we build it — data boundaries, review points, and audit trail designed in, not bolted on.
03
Build
The pipeline ships against the evaluation set from week one, with accuracy tracked per field and the review UI built alongside the model work.
04
Scale
Drift monitoring, monthly accuracy audits, and cost dashboards — then handover, with your team owning prompts and thresholds through change control.
Evaluate
a feasibility phase before committing to the build
Reviewed
a person approves anything that reaches a system of record
Pinned
prompts and model versions under change control
How every engagement is run — operating commitments, not past-client results.
Case study
What that looks like shipped
Indian F&O traders keep journals badly or not at all, so they never see what their behavioural patterns cost. pnlbook uses AI to turn a broker screenshot into a logged trade, prices each rule violation in rupees, and writes a weekly coaching letter grounded in the trader's own ledger.
Read the full case study
In depth
AI workflows with review built in
Most enterprise AI projects don't fail on model quality — they fail in a conference room, when compliance asks three questions the demo was never built to answer: where does the data go, who is accountable for each output, and how do you know it's right. An AI development company that can't answer those questions in architecture, rather than assurances, is building you a very expensive proof of concept.
A reviewable system needs three foundations. Evaluation against a hand-labeled baseline, so accuracy is a measured number per field rather than an impression. Human review for decisions where errors matter. And documented data boundaries, with sensitive content excluded from logs and access restricted to the people and systems that need it.
That's the shape of the extraction pipeline we built for pnlbook, a trading journal that reads a broker screenshot into a strict schema of symbol, quantity, and entry and exit prices. Values the model is unsure about are flagged for a human to confirm rather than written silently, so a person owns every number that lands in the ledger — the same human-in-the-loop pattern that lets a system like this stand up to scrutiny instead of asking to be trusted.
The unglamorous half of LLM integration services is everything around the model: classification and extraction upstream, schema and plausibility validation on every field, posting to the system of record downstream, and a review UI fast enough that confirming a value takes one glance instead of a re-key. The model is only one part of the implementation. Teams that treat it as the whole product are the teams whose pilots never leave the lab.
When not to use AI — and what to build instead
A useful test before any engagement: can you write down the rules? If a person can enumerate the logic — route claims above a threshold to a senior adjuster, flag invoices that don't match a purchase order — you want deterministic code, not a model. It's cheaper to run, trivial to test, and explains itself in an audit. When a struggling LLM prototype fails this test, the fix we propose is a few hundred lines of validation logic — not a better prompt.
LLMs also lose when volume is low and error cost is high. A team handling thirty documents a day doesn't need a pipeline, and building the review machinery to catch rare, expensive mistakes can cost more than the mistakes. And where no ground truth exists, nobody can label a baseline, which means nobody can measure accuracy, which means you would be shipping something you can't defend. That's a no.
This is why we scope a feasibility phase before committing to a larger build. We build the labeled evaluation set, measure the accuracy ceiling on your real documents, and model the unit cost. The phase is a genuine kill gate: when the numbers say AI is the wrong tool, the project ends there — a decision that can avoid spending on an unsuitable solution.
Measuring AI ROI in cycle time, not benchmarks
Model benchmarks don't appear on a P&L. The number that survives an executive review is cycle time: minutes per document, days of backlog, hours from submission to a finished result. Before we write a prompt, we baseline the workflow — how long each step takes, where the queue builds, what a fully loaded hour of the person doing it costs — because ROI is a before-and-after comparison, and you need the before.
That framing is what turns a model into numbers finance can use: minutes saved per document, backlog days cleared, time-to-outcome shortened. Accuracy against a labeled set belongs in the monthly audit, but it is rarely what funds the work — the cleared backlog and the freed headcount are. We instrument the before and the after so the claim is a measured delta, never an impression.
Unit economics get the same treatment. Cost per document is modeled in the feasibility phase — model calls, review minutes, infrastructure — and compared against the manual cost it replaces. Routing keeps it honest: a small model classifies, a larger one extracts, caching absorbs the repeats. If the pipeline plus human review costs more per document than the person it assists, we will tell you, because shipping it anyway just moves the loss onto a dashboard.
Tools we reach for
A proven, hireable stack
Chosen so your team can maintain, extend, and hire for everything we leave behind.
- Python
- TypeScript
- PostgreSQL
- pgvector
- AWS Bedrock
- FastAPI
- Docker
- Terraform
FAQ
The questions buyers actually ask
Answered the way we’d answer them on a call — specifics included.
Begin with a scoped feasibility review using representative inputs and agreed evaluation criteria. We quote the work in INR and separate model usage, hosting and review costs. A larger build proceeds only after the findings and scope are reviewed.
The agreement defines ownership of project code, prompts and evaluation assets, subject to the rights in source data and third-party models. We recommend using business-owned accounts and documenting provider dependencies.
We work on India Standard Time and agree review sessions with the people who understand your workflow. Those domain experts help define correct outputs, review exceptions and approve acceptance criteria.
That depends on sample availability, document variation and access to domain experts. We agree the feasibility scope and schedule once those inputs are understood. The findings describe tested examples and remaining uncertainty.
We map the data flow with your team, identify what each provider receives and agree retention, access and logging boundaries. Provider terms and any specialist compliance requirements must be checked for the specific use case before implementation.
Compare results with the agreed evaluation set and route uncertain outputs to human review where appropriate. The next step may be a narrower scope, a different approach or stopping the build. Monitoring and later quality reviews are scoped explicitly.
No. What we hand over is prompts, schemas, and thresholds under change control — closer to configuration than research. A backend engineer on your team can own it after handover, supported by a runbook and the monthly accuracy audit, which is a report to read rather than an experiment to run. If a change ever does need model work — a new document type, a new language — that's a scoped engagement, not a standing team.
The pipeline treats the model as a swappable component behind one interface, and the evaluation set is your insurance policy: point a candidate model at it, compare per-field accuracy and cost, and promote it only if it wins. Model versions are pinned, so upgrades happen when you choose them, not when a vendor pushes them. Model churn becomes a routine eval run instead of a rewrite.
Cost per document is modeled in the feasibility phase and tracked on a dashboard from the first week of the build — model calls, retries, review minutes, infrastructure. The engineering that keeps it flat is routing and caching: a small model handles classification, the large one runs only where extraction is hard, and repeated content never pays twice. You'll know the unit cost curve before you commit to volume, and the dashboard flags drift in spend the same way the audit flags drift in accuracy.
Start with the task your team repeats. For website enquiries, appointment coordination and CRM follow-up, see our [business automation service](/ai-automation) for Indian SMEs. We check your existing tools and quote a focused workflow in INR. Custom AI is considered only where it adds value beyond ordinary forms, rules and integrations.
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