Ideal For
Who this engagement fits best
- Product teams adding AI features without an in-house ML team
- Companies sitting on proprietary data they want to unlock
- Founders validating an AI-native product hypothesis
- Engineering orgs replacing brittle prompt scripts with production pipelines
Not quite the right fit? Browse other services or reach out and we'll figure it out together.
Outcomes
Results clients see
Average reduction in manual review time after RAG deployment
Typical end-to-end latency for production retrieval pipelines
Cost target after caching, routing, and prompt optimization
On guarded responses with retrieval grounding and eval gates
See similar results in the case study archive.
Process
How we work together
A structured approach to ai engineering that delivers results.
Discovery
Understand your use case, data landscape, and success metrics
Architecture
Design the optimal AI system architecture for your constraints
Prototype
Build and validate core AI capabilities with real data
Production
Scale, optimize, and deploy with monitoring and guardrails
Curious what each phase looks like in detail? Read the full process page.
Deliverables
What you get
Tangible outcomes from every engagement, not just slides.
Every deliverable is owned by you on day one—your repo, your cloud, your accounts. Want to see real artifacts from past engagements? Visit the work archive.
Pricing
From $18,000 / 4 weeks
Scoped engagements for AI prototypes, retainers for ongoing model and pipeline ownership.
Engagement model: Project-based or monthly retainer
- Use-case scoping
- Data audit
- Architecture proposal
- Build vs. buy recommendation
- End-to-end RAG or agent build
- Eval harness + dashboards
- Deployment + handover
- Two weeks of post-launch tuning
- 2 days/week of senior AI engineering
- Roadmap ownership
- Model monitoring
- Async team coaching
Need a custom scope? See full pricing details or request a custom quote.
Client Voices
What teams say
Anonymized quotes from recent engagements.
“The retrieval pipeline he shipped replaced six weeks of manual document review every quarter. We finally trust the answers.”
“Sri took us from a flaky prompt-chaining demo to a hardened production agent in under two months. Evals included.”
Technologies
Tools and platforms
The core technologies I use for ai engineering projects.
Want to go deeper on any of these?
FAQs
Frequently asked questions
Answers to the most common questions about ai engineering engagements.
Do I need clean data before we start?
No. The discovery sprint includes a data audit and we usually ship the first useful version on messy data. Cleaning happens in parallel.
Which model providers do you work with?
Primarily Anthropic, OpenAI, and open-weights via Bedrock or self-hosted vLLM. Routing across providers is part of most production builds.
Can you work with our existing ML team?
Yes-most engagements are collaborative. I focus on the LLM and retrieval layer while your team owns classical ML and data.
Have a question that isn't here? Ask directly—I reply personally to every message.
Industries
Where this work lands
Sectors where this service has shipped real outcomes.
Reading
Related insights
Posts on topics adjacent to this engagement.
Or browse all insights.
Related
You might also need
Services that work well together with this engagement.
Ready to get started?
Let's discuss how ai engineering can help your business. Most projects kick off within two weeks of the first call.