GorkhaLabs
AI development engineered for production—not demos
Large language models unlock powerful experiences, but production AI fails on evaluation gaps, prompt drift, unsafe outputs, and unpredictable costs. We ship AI features with the same operational discipline as payments: versioning, monitoring, rollback levers, and clear ownership when models misbehave.
What we build in AI engineering
We implement retrieval-augmented workflows, summarization pipelines, copilots embedded into SaaS consoles, and ranking assistants that augment—not replace—human judgment. Each system is designed with explicit failure modes: timeouts, fallbacks, and user-visible uncertainty when confidence is low.
Evaluation is continuous: golden sets, regression suites for prompts, and offline scoring paired with online metrics that map to business outcomes (task completion, support deflection, operator throughput).
For teams in Siliguri and across India building health, logistics, or education products, we align AI outputs with consent, retention, and regional language realities—so features remain trustworthy as you scale.
- Provider routing with budgets and circuit breakers
- PII minimization and redaction patterns
- Tracing for prompt/tool chains
- Admin review queues where required
Why production AI needs a product engineering partner
Model capability is only one ingredient; the rest is UX, data quality, and operational tooling. We bridge ML enthusiasm with shipping discipline so your roadmap stays credible with stakeholders and regulators.
Typical architecture patterns
We commonly pair vector stores with structured databases, cache embeddings intelligently, and isolate model calls behind typed service boundaries. Streaming responses are handled with backpressure and client-side rendering patterns that keep UX snappy.
Risk management and governance
We document data flows, retention, and human oversight requirements. For sensitive domains, we implement access controls, audit logs, and separation of duties so AI-assisted actions remain accountable.
Roadmap: from pilot to scale
We recommend starting with narrow pilots tied to measurable KPIs, then expanding surface area as evaluation coverage grows. That prevents “AI everywhere” debt that becomes unmaintainable.
We also help you plan cost curves as traffic grows—batching, caching, model selection, and distilled models where appropriate.
How we deliver
- 1
Use-case shaping
Define tasks, success metrics, and safety constraints with stakeholders.
- 2
Baseline & retrieval
Data audit, chunking strategy, and evaluation harness before scaling features.
- 3
Product integration
UX flows, admin tooling, telemetry, and guardrails in the real app shell.
- 4
Operate & improve
Incident playbooks, drift monitoring, and periodic model/provider reviews.
Technology stack
- Python
- Node.js
- TypeScript
- OpenAI / Anthropic APIs
- LangChain-style patterns
- pgvector
- Redis
- OpenTelemetry
- AWS
Frequently asked questions
- Do you train custom models?
- We focus on applied integration, fine-tuning when justified, and evaluation—full foundation model training is typically out of scope.
- How do you protect user data?
- Minimize retention, encrypt in transit/at rest, isolate secrets, and design prompts so sensitive data is not unnecessarily sent to providers.
- Can AI features ship without a dedicated ML team?
- Yes—when scope is bounded and evaluation is treated as an ongoing product responsibility, not a one-time benchmark.
Continue exploring
Consultation
Tell us about your roadmap
Scope, timeline, and success metrics—we reply within one business day with clear next steps.