AI Engineering Studio · 2026

Production AI,
not science-fair
demos.

28 Models in production
∼220ms Median agent latency
4–8 wk Pilot to production
99.95% Inference uptime SLO

What we build

Capability-led, not framework-led. Each engagement is scoped to a measurable outcome — accuracy, latency, deflection rate, conversion lift, or cost-per-task — before a model gets selected.

01 // Flagship capability

LLM applications & copilots

Internal copilots, customer-facing assistants, structured-output extractors, and embedded GPT experiences. Built on the right model for your latency and cost envelope, with prompt versioning, eval harnesses, and guardrails wired in from day one.

// eval/extract.test.ts
expect(extract(invoice)).toMatch({
  total: '1,420.00',
  currency: 'INR',
  due:     '2026-06-14',
});
// 96.2% pass · 1,400 sample fixtures
ClaudeOpenAILlama 3.xGeminiMistral
02

Retrieval (RAG) & knowledge systems

Hybrid retrieval, reranking, structured chunking, and answer grounding. Built for your sources, your taxonomy, and your update cadence.

pgvectorQdrantWeaviateCohere Rerank
03

Autonomous agents

Tool-using agents that plan, call APIs, escalate to humans, and stay within guardrails. Designed around your real workflows.

Tool useHITLTracing
04

Voice & speech AI

Real-time transcription, voice agents, and STT/TTS pipelines tuned for Indic accents and noisy environments.

WhisperDeepgramElevenLabs
05

Computer vision

Detection, segmentation, OCR, and document AI. From in-store analytics to defect inspection on the line.

YOLOSAMVLMs
06

Evaluation & observability

LLM-as-judge harnesses, golden-set CI, prompt regression tests, and tracing for every span. Your product can't improve what it can't measure.

LangFuseBraintrustCustom evals
07

Inference infrastructure

Self-hosted or hybrid stacks on AWS / GCP. Quantisation, batching, autoscaling, and cost ceilings, with 99.95% uptime SLOs.

vLLMTritonModalBedrock
08

Fine-tuning & distillation

SFT, DPO, and small-model distillation for your domain. We size the model to the task, not the headlines.

LoRAQLoRADPORLHF

How an engagement actually runs

Two-week discovery, eight-week build, with weekly demos and a single owner on each side. No black boxes, no quarterly reveals.

PHASE 01 / WEEK 0–2

Discover & scope

Workshop the workflow, define the success metric, audit data, pick the model class, draft the eval set.

PHASE 02 / WEEK 2–4

Prototype

Working slice in your stack, end-to-end, with the eval harness running against the golden set.

PHASE 03 / WEEK 4–8

Productionise

Hardening, guardrails, observability, cost guards, and operator runbooks. Loaded behind your auth.

PHASE 04 / ONGOING

Operate

On-call, regression tests on every prompt change, monthly accuracy reviews, and improvement cadence.

The stack

Tooling we reach for. We'll happily work in yours instead — every choice below is replaceable.

Models

FRONTIER + OSS
Claude 4.6GPT-4oGemini 2.0Llama 3.xMistralQwen

Retrieval

VECTOR + HYBRID
pgvectorQdrantWeaviatePineconeCohere RerankBGE

Orchestration

AGENT + WORKFLOW
LangGraphInngestTemporalOpenAI Agents SDKCustom

Eval & observe

CI FOR LLMS
LangFuseBraintrustPhoenixOpenTelemetryCustom harnesses

Infra

SERVE + SCALE
vLLMTritonModalAWS BedrockVertex AIReplicate

Where it pays back

Selected from real engagements. Numbers reflect post-launch measurement, not vendor claims.

FINTECH · KYC
Document extraction agent

Replaced manual KYC review on 8 form types. Structured-output extraction with rejection routing for low confidence.

96.2% accuracy / 12× faster
D2C · SUPPORT
Tier-1 support copilot

RAG over policies, returns, and order status. Deflects routine tickets, escalates the rest with full context.

42% deflection / NPS +6
B2B · SALES
Lead enrichment agent

Researches accounts, drafts outreach, logs to CRM, and routes hot leads to AEs in real time.

3.4× pipeline / 70% time saved
HEALTH · CLINICAL
Voice-to-note agent

Indic-accent transcription with structured medical-note generation, reviewed by clinicians before save.

11 min saved / consult
RETAIL · OPS
Shelf compliance vision

Camera-fed shelf detection across 240 stores, surfaced in a daily compliance dashboard.

+8% planogram score
EDTECH · CONTENT
Tutor agent with guardrails

Subject-scoped tutor with hint-laddering, refusals on out-of-scope queries, and parent dashboards.

1.7× session time
"
The team shipped a working agent in 6 weeks that our internal team had been chasing for nine months. Evaluation harness on day one was the difference.
RK
Rohan Kapoor · VP Engineering, MediaPulse Studios

How we engage

Three shapes. Pick the one that matches the stage you're in.

01 / DISCOVERY

Sprint

Two weeks. We scope the problem, audit data, build a thin prototype, and hand back a go/no-go.

  • Workflow workshop
  • Eval set + golden fixtures
  • Prototype + cost model
  • Build / no-build memo
Two weeksFrom ₹4.5L
02 / DELIVERY · most popular

Build

Eight to twelve weeks. End-to-end engagement with weekly demos and a production handover.

  • Everything in Sprint
  • Production-grade build
  • Observability + guardrails
  • Runbooks + on-call handover
8–12 weeksFrom ₹18L
03 / RUN

Operate

Ongoing. We run the system with you: regression tests on prompt changes, accuracy reviews, on-call.

  • Monthly accuracy review
  • Prompt + model rev cadence
  • SLO-backed on-call
  • Quarterly roadmap
Per monthFrom ₹3.5L

Honest answers

The questions every prospect actually asks. Direct answers, no agency hedging.

What if our use case is too small for AI?

The Sprint exists for that. Two weeks, fixed price, and we send you a memo telling you not to build it if that's the truth.

Will my data train someone's model?

No. We default to API providers with zero-retention policies, or self-hosted models on your infra. Spelled out in the contract.

Can you work in our existing codebase?

Yes. We work in your repo, your stack, your CI. We're not allergic to TypeScript, Python, Go, or whatever else you ship.

How do you measure if it's working?

Eval harness on day one. Golden set, regression on every prompt change, structured logging on every span. The metric is set with you in week one.

Frontier model or open source?

Whichever wins on your eval. We start with the strongest model that fits your latency and cost ceiling, then distil down only if numbers force it.

What happens after launch?

Operate. Models drift, the world changes, your data evolves. We run regression tests, accuracy reviews, and SLO-backed on-call.

Ship one working AI feature, in eight weeks.

Walk us through the workflow you want changed. We'll come back with a scope, a price, and the metric we'll be accountable to.

Start a brief Talk on WhatsApp