Who this is for
The right fit
- Enterprises whose board has asked for an AI strategy
- Companies whose AI pilots stall before production
- Teams trying to operationalize ChatGPT-style proofs
- Orgs whose data is locked up across silos
What you can expect
Outcomes that matter
>70%
Pilot-to-production rate
vs. industry ~20%
8-12 weeks
Time to first AI value
for the lighthouse use case
5-15 use cases
AI platform reuse
on shared foundations
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
- •Unclear where AI adds real value
- •Many AI projects fail to reach production
- •Team lacks AI/ML expertise
- •Data not ready for AI applications
Approach
- 1.Audit processes for AI automation potential
- 2.Prioritize high-value, feasible use cases
- 3.Build foundational data infrastructure
- 4.Develop internal AI capabilities
Outcomes
- Clear AI strategy aligned with business goals
- Production AI systems delivering value
- Team capable of building and maintaining AI
- Data infrastructure ready for AI applications
How we work
Engagement phases
A predictable rhythm from kickoff to handoff. Phases overlap when it makes sense.
AI Audit
Map every workflow against AI's current strengths and score each by value and feasibility.
- Use-case portfolio
- Feasibility scoring
- AI strategy doc
Lighthouse
Ship one production-grade use case to set the bar for evals, ops, and ROI.
- Production lighthouse
- Eval framework
- ROI baseline
Platform
Stand up the shared platform: data pipelines, vector stores, model gateway, governance.
- AI platform
- Data foundation
- Governance and policy
Scale & Capability
Train internal teams, run a backlog of use cases, measure value, kill what doesn't work.
- Team enablement
- Use case roadmap
- Quarterly value review
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
Knowledge work cycle time -85%
Intelligent Search Platform
Enterprise search NPS +40 pts
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.
Should we build or buy AI features?
Buy the commodity (general chat, transcription), build where the data and workflow is yours. The audit makes that call concrete.
How do we keep AI from hallucinating in production?
Retrieval grounding, structured outputs, evals on every change, and explicit user-facing affordances when the model is unsure.
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.