Node.js 
Python 
Go 
Java 
C# 
AWS 
GCP 
Azure 
Vercel 
Netlify 
PostgreSQL 
MongoDB 
Redis 
BigQuery 
Snowflake 
TensorFlow 
PyTorch 
OpenAI 
Anthropic 
Gemini 
NotebookLM 
ElevenLabs 01
Discovery Sprint
A 2-week deep dive that ends with a real plan and a real number.
Choose this if you need clarity before committing.
Start a SprintSystem Uptime99.97%Throughput2.4M req/sActive Nodes47Last Deploy14m agoQueue Depth12Build StatusPASSLatency p9923msError Rate0.02%System Uptime99.97%Throughput2.4M req/sActive Nodes47Last Deploy14m agoQueue Depth12Build StatusPASSLatency p9923msError Rate0.02%
— SERVICES / AI-DATA
AI & Data.
Models and pipelines built to survive production.
Production AI, not prototypes. Pipelines, models, and monitoring from the first sprint.
— WHAT WE DELIVER
Concrete outcomes.
- 01Production ML or LLM workflowModel deployed behind a versioned API, with prompts, fine-tuning data, or weights all version-controlled.
- 02Data pipeline infrastructureAirflow or dbt for batch, streaming where it earns its keep. Schema-validated, observable, idempotent.
- 03A RAG or agent system that worksEmbedding pipeline, retrieval evaluation, system prompts, guardrails. Fall-back behaviour when the model stalls.
- 04Drift + cost monitoringModel performance, token spend, and data drift dashboards. Alerts when accuracy or budget moves.
- 05Evaluation harnessGolden test set, scoring rubric, and CI gate so model changes don't silently regress quality.
— WHAT WE DON'T DO
Honest about the limits.
- Ship a demo and call it production — every model lands with monitoring, fallback paths, and an eval suite.
- Use the largest model when a smaller one fits — we measure, then pick. Cost is a feature.
- Generate plausible nonsense — RAG without retrieval evaluation is a liability. We measure groundedness.
— HOW WE DELIVER
Five stages, weekly demos.
- 01Discovery AuditDeep-dive into your domain, systems, and constraints.
- 02Strategy DesignArchitecture, UX, and the technical roadmap.
- 03Development BuildSprint-based delivery with weekly demos.
- 04Testing OptimizeQA, performance tuning, and security review.
- 05Deployment LaunchProduction release with monitoring and handover.
— TECH WE LEAN ON
Tools that fit the work.
Backend
Node.js Python Go Java C#
Cloud
AWS GCP Azure Vercel Netlify
Data
PostgreSQL MongoDB Redis BigQuery Snowflake
AI / ML
TensorFlow PyTorch OpenAI Anthropic Gemini NotebookLM ElevenLabs
— RECENT WORK
Where this has shipped.
— COMMON QUESTIONS
Answers before you ask.
01OpenAI, Anthropic, or open-source?
We work across all of them and ship multi-provider via Vercel AI Gateway when latency or cost matters. The model is an implementation detail; the eval harness is what stays.
02How do you handle hallucinations?
Retrieval-grounded responses with citations, structured-output validation via Zod schemas, and a per-response confidence check. Where stakes are high, we add a human review queue.
03Do you fine-tune or just prompt?
Both, depending on the workload. Fine-tuning earns its keep when prompt engineering plateaus and you have ≥1K labeled examples. We measure before deciding.
04What about data privacy?
On-prem deployments via Bedrock, Azure OpenAI, or self-hosted Llama where required. We never send data to a model your contracts don't cover.
— 03 ENGAGEMENT MODELS