Who this is for
The right fit
- Product teams shipping their first AI feature
- Founders evaluating AI as a product differentiator
- Companies whose competitors are racing ahead with AI
- SaaS platforms looking to expand their feature surface
What you can expect
Outcomes that matter
6-10 weeks
Time to first AI feature
from kickoff to production traffic
>95%
Eval pass rate target
before any prompt change ships
40-70%
Inference cost cut
via routing, caching, smaller models
Want a deeper benchmark? See real numbers in client work or read engineering insights.
Anatomy
Challenges, approach, outcomes
The core shape of every engagement.
Challenges Addressed
- •Identifying where AI adds genuine value vs. hype
- •Building reliable AI features that work at scale
- •Managing costs and latency of AI operations
- •Ensuring AI outputs meet quality standards
Approach
- 1.Map user journeys to identify high-impact AI opportunities
- 2.Prototype and validate with real users before full build
- 3.Design for graceful degradation and edge cases
- 4.Implement evaluation frameworks for continuous improvement
Outcomes
- Differentiated product with defensible AI capabilities
- Improved user engagement and satisfaction
- Reduced manual work through intelligent automation
- Foundation for continued AI innovation
How we work
Engagement phases
A predictable rhythm from kickoff to handoff. Phases overlap when it makes sense.
Opportunity Mapping
Audit your product surface and pinpoint AI moments that matter to users.
- AI opportunity scorecard
- Prioritized backlog
- Cost & latency model
Prototype & Validate
Ship a working slice in front of real users to confirm value before scaling.
- Working prototype
- Eval harness
- User feedback report
Production Hardening
Build observability, fallbacks, and guardrails so the feature ships safely.
- Observability dashboards
- Fallback paths
- Prompt/version registry
Iterate & Expand
Tighten the eval loop and ship adjacent AI experiences with confidence.
- Eval datasets
- A/B framework
- Roadmap of next features
Curious how this maps to your context? Walk through the engagement process or jump straight to scoping a project.
Services
Services that deliver this solution
The capabilities Sri brings to bear on this engagement.
Stack
Technologies in play
The tools Sri reaches for when delivering this solution.
Industries
Best fit for
Sectors where this solution delivers the most value.
Proof
Recent work
Where this solution has delivered for real teams.
AI Document Processor
10,000+ docs/day with 99.2% accuracy
Intelligent Search Platform
3x search engagement, 40% deflection
Browse the full case study library or see who Sri has worked with.
Dig deeper
Further reading
Playbooks, blueprints, and writings that go deeper on this solution.
FAQ
Common questions
What founders and engineering leaders ask before kicking off.
Do I need a data science team to start?
No. Most product-AI work today is engineering, not modeling. I focus on retrieval, evals, and product wiring so a strong app team can ship.
How do you keep AI costs predictable?
Routing between model tiers, aggressive caching, and budget guardrails per request. Cost shows up on dashboards from day one.
More questions? Check pricing and engagement models or ask Sri directly.
Adjacent
Related solutions
Often paired with or sequenced after this engagement.
Ready to implement this solution?
Let's discuss how this approach can be tailored to your specific needs.