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Opportune

Beyond traces. Ship reliable LLM workflows.

A hands-on AI engineering partner for context-heavy workflows. We find the reliability, grounding, and token-cost failures specific to your business — and fix them with your team.

Take the production auditor speak to us →
[01]problem

AI systems do not fail like normal software.

They're probabilistic. They're context-sensitive. They work in demos and often break in production. And when they break, they don't throw errors. They just give worse answers, eat more tokens, and erode user trust until someone notices.

[02]how we find it

We go through your stack line by line and show you where money is going — and why.

Most LLM cost waste is invisible until you map it. The same context problems that drive up your bill are also what cause your AI to give worse answers at high load, forget instructions mid-conversation, or regress silently after a config change. Cost and reliability are the same problem from two angles.

↳
Context design
What is actually in the context window, what is noise, what is missing
↳
RAG efficiency
Retrieval quality, chunk sizing, re-fetch rate, caching opportunities
↳
Model routing
Which tasks use frontier models when a smaller model would be identical
↳
Token attribution
Cost broken down by feature, call type, and team — the number your CFO wants
↳
Retry overhead
Token spend from hallucinations, failed tool calls, and retry loops
↳
Config integrity
Temperature, model version, prompt changes that silently shift output quality and cost
[04]services

How we engage

[01]

AI Cost and Reliability Diagnostic Audit

Timeline

1–2 weeks

Format

Fixed scope

A full-stack cost and reliability diagnosis. We go through your stack line by line — context design, RAG usage, model routing, retry patterns, and config — and produce the breakdown your CFO has never seen.

Deliverables

LLM cost per feature, call type, and user segment. Top 3 recoverable waste items with specific dollar figures. Context window audit. Reliability risk map across all eight dimensions.

Outcome

A precise dollar figure for recoverable waste. The internal business case to fix it.

[02]

AI Cost and Reliability Fix Sprint

Timeline

2-8 weeks

Format

Scoped from diagnostic

Implement the fixes identified in the diagnostic. Context compression, query planners, RAG caching, model routing, eval harness, config integrity layer, cost attribution telemetry. Scoped from the findings.

Deliverables

Architectural fixes shipped with your team. Eval harness in CI. Observability layer. Provider fallback routing. Per-feature cost attribution. Rollback runbooks.

Outcome

Recoverable waste eliminated. The instrumentation to catch the next drift before it reaches clients.

[03]

Reliability Partner

Timeline

Ongoing

Format

Monthly retainer

The eval framework, failure taxonomy, and reliability runbooks become part of your stack. We run them, monitor thresholds, and respond when something drifts — before your users notice.

Deliverables

Monthly reliability review. Threshold monitoring. Incident response. Quarterly cost re-audit as traffic grows.

Outcome

AI unit economics that hold at 10× scale.

Speak to us

Minimum engagement $2,500 · Fix Sprint scope and timeline are determined from Diagnostic findings. Retainer begins after the Fix Sprint.

[04]team

A senior team bringing together production-scale systems engineering, AI reliability research, and hands-on technology services experience.

Rohan Jahagirdar

Rohan Jahagirdar

Expert in Product Management15+ Years of Experience
Founder · Delivery and Operations

Rohan brings 15+ years across technology, marketing, and venture building, including 6+ years running service contracts for MUFG-IS, Stanley Black & Decker, Teachable, and Azerili. He previously co-founded a fintech company that built mission-critical API systems for publicly listed banks and AMCs.

Worked with

FlipkartTeachableMUFG
Ramanan Sivasubramanian

Ramanan Sivasubramanian

Expert in Reliability & AI Engineering11+ Years of Experience
Founder · Engineering

11+ years building reliable systems at WhatsApp and TikTok. Former IIT Hyderabad researcher on AI accuracy for Indic and non-Latin scripts — transliteration, healthcare-domain transformers, hallucination reduction. Two masters degrees from IIT Hyderabad and BITS Pilani.

Previously at

WhatsAppTikTokAmazon
[05]common questions

Between 40–60% of token spend is recoverable in most production stacks. The most common sources: context that never influences the output (full documents when chunks would do), conversation history that should be compressed, RAG results fetched repeatedly without caching, and frontier models handling structured tasks a smaller model would handle identically. The diagnostic gives you the specific number for your architecture.

[next step]~5 min · free · no commitment

Find out what your AI stack is actually costing you.

A branched cost audit — ~5 minutes, specific to your architecture and scale. You get a cost risk score and your highest-impact optimization areas.

Take the free cost auditor speak to us directly →
[lineage]

Saarthi, our patient-facing health AI, is where we stress-test the techniques we now bring to AI-native companies.

saarthihealth.com