AI products that survive production.
Most AI initiatives die between the notebook and the release branch. We engineer the full path — problem framing, model selection, eval harnesses, serving infrastructure, and the unglamorous 80% that makes AI dependable.
✓ ship rate: 50+ products · retention: 98%
✓ ip transfer: complete · lock-in: none
✓ delivery: hyderabad · timezone overlap: US/EU
# every claim on this page is contractually testable
The demo-to-production gap is where AI budgets quietly disappear.
A prototype that wows a steering committee shares almost no code with a system that serves 10,000 users at p95 < 400ms with a rollback plan. The gap is evals, observability, cost ceilings, failure modes, and data contracts. That gap is precisely what we build.
Capabilities
Product discovery & framing
We pressure-test whether AI is the right tool before writing code — defining the metric that matters, the baseline to beat, and the cost ceiling per request.
Model selection & evals
Frontier API, open-weights, or fine-tune — decided by your latency, privacy, and unit-economics constraints, then locked in with a regression eval suite, not intuition.
Serving & infrastructure
Inference gateways, caching, batching, autoscaling, and fallbacks. Token budgets enforced in code. Multi-provider failover as standard.
Observability & guardrails
Traces on every request, drift monitors on every input distribution, and guardrails (PII, jailbreak, topicality) that run inline — not in a postmortem.
Launch & iteration
Canary rollouts gated on eval scores. Post-launch, we tune against real traffic and hand over a system your team can run without us.
Full IP transfer
Everything we build — code, evals, infra-as-code, runbooks — is yours. No license lock-in, no black boxes.
The approach
A sequence, because the order is the point: each phase gates the next on evidence.
Frame
One to two weeks. We define the success metric, the non-AI baseline, the unit-cost ceiling, and the kill criteria. If the business case doesn't survive this, you've spent two weeks, not two quarters.
Prove
A thin vertical slice against real data with an eval harness from day one. We measure, not estimate — accuracy, latency, and cost per request on your actual workload.
Build
Production engineering: serving infra, guardrails, observability, CI/CD with eval gates. Weekly demos against the metric defined in step one.
Ship & harden
Canary rollout, load testing, failure injection, runbooks. We stay through the first month of real traffic and transfer ownership deliberately.
Deliverables
- Production codebase with full IP transfer
- Versioned eval suite with CI regression gates
- Infrastructure-as-code (Terraform/Helm)
- Inference cost model and budget alerts
- Observability dashboards and alert runbooks
- Architecture decision records (ADRs)
- Guardrail configuration and red-team report
- 30-day post-launch hardening period