AI features that survive their first quarter in production.
Most AI demos don't ship. The ones that ship rarely survive real usage. We build production AI with the same evaluation rigor we apply to any other critical system — because users don't grade demos.
Data platforms & production AI
Data engineering (pipelines, warehouses, streaming) and production AI (RAG, agents, copilots, LLM-powered features) delivered together. Evaluation, observability, and cost management are treated as first-class engineering concerns, not optional extras.
Fit signals.
- You have an AI proof-of-concept that works in a demo but nobody trusts in production
- A warehouse exists but analysts wait days for a trustworthy number
- You want to ship LLM features without betting the company on hallucinated outputs
- Real-time or near-real-time analytics are in the product roadmap
- You're paying for every token and the answer quality is declining
What we deliver in Data & AI.
Every capability below is practiced across multiple production engagements — not a scoping checklist.
Data engineering
- Snowflake, BigQuery, Databricks — picked by team + workload
- dbt + semantic layer for analytics-engineering discipline
- Streaming (Kafka, Kinesis, Pulsar) where batch can't win
- Reverse ETL + activation layers (Hightouch, Census, custom)
Production AI
- RAG with hybrid retrieval (vector + keyword + structured)
- Agents with tool-use, memory, and clear boundaries
- Evaluation pipelines — offline + online, LLM-as-judge + human
- Observability (LangSmith, Helicone, custom) wired from day one
Model strategy
- Claude (Sonnet 4.6, Opus 4.6) as default
- Mixed-model routing for cost + latency tuning
- Prompt caching, tool-use, extended thinking patterns
- Fine-tuning + small-model serving where warranted
Analytics & ML
- Forecasting, recommendation, anomaly detection
- Feature stores + model registries when scale warrants
- ML experiment tracking (MLflow, Weights & Biases)
- On-device model inference (Core ML, TFLite)
The shapes this work
usually takes.
Production AI feature
Typical: 12–16 weeks. One well-scoped LLM feature shipped with evaluation, observability, and guardrails.
Data platform rebuild
Typical: 16–24 weeks. Warehouse + dbt + activation layer + analytics-engineering culture.
AI cost optimization
Typical: 6–10 weeks. Routing, caching, and model-mix strategies that cut spend 40–70% without quality loss.
Data & AI managed service
Monthly. Pipeline hygiene, model performance reviews, ongoing feature delivery.
Pitfalls we've seen
and how we avoid them.
Demo-to-production gap
Ship the demo, skip evaluation. Users find the hallucinations in week one. We wire eval pipelines before we wire features.
Single-model lock-in
Every call to the most expensive model. Costs spiral. Routing + caching + mixed models cut bills without hurting quality.
RAG without retrieval quality
Vector search tuned once, never measured. Retrieval precision silently decays. We measure it continuously.
Warehouse as the only answer
Everything modelled as daily batch. Real-time use cases starve. Pick streaming where the product demands it.
Common questions about Data & AI.
Claude is our default for production agent and RAG work — we've found Sonnet 4.6 is the best cost/quality fit for most workloads and Opus 4.6 earns its price on complex tool-use. We'll route to OpenAI or Gemini where they genuinely win, and run open-source models (Llama, Mistral) when cost or data-residency requirements drive it.
Data & AI on your roadmap?
Thirty minutes with Kabir. Architecture sketch, candid second opinion, scope estimate — no slides.
Book the call