<200ms
p99 AI response latency
Fits inside existing UX latency budgets — no added spinners, no degraded experience for end users.
Semantic cache absorbs repeated queries before they reach the LLM; async call patterns keep the model call off the critical request path.
Holds when: measured at your API boundary, steady-state traffic, cache warm. Cold start and first-token latency are scoped and reported separately.
40–70%
LLM token cost reduction
Spend scales sub-linearly as usage grows — each cache hit costs near-zero versus a live model call.
Embedding-similarity cache normalises paraphrase variants; cosine similarity > 0.92 bypasses the LLM entirely before the request leaves your infrastructure.
Holds when: compared against your own current per-request spend, on a workload with repeating query patterns. The range tracks cache hit rate — low-repetition workloads land at the bottom of it or below, and we tell you which one you are during the audit.
7–13 wks
Audit to production
Fixed scope means predictable delivery — no open-ended retainer creep, no scope ambiguity after kickoff.
Architecture audit scopes every task before code is written; feature flags enable incremental ship, not a big-bang launch with a single rollback point.
Holds when: scope is fixed after the audit and your team is available for review cycles. Breaks down as audit 1–2, scope 1, engineering 4–8, rollout 1–2 weeks.
3 modes
Graceful degradation on LLM failure
Provider outage or rate limit becomes a handled edge case, not an on-call incident.
Circuit breakers at every outbound call with three failure paths: serve cached response, queue for async retry, or return a typed degraded result — no exception propagation to the user.
Applies to: every outbound model call in the integration we build. Which mode fires for which failure is agreed in the scope document, not chosen at runtime.
100%
AI interaction audit coverage
Every AI interaction is attributable — who triggered it, what was retrieved, and what the model returned.
Structured log per call: tenant ID, user ID, prompt hash, retrieved document IDs, response token count — piped to your existing log sink from day one.
Covers: AI interactions in the system we build. Pre-existing services are included only if we instrument them during the engagement — that is an explicit scope line.
3+
LLM providers, zero lock-in
No single vendor's pricing, rate limits, or availability controls your production system.
Provider abstraction layer normalises OpenAI, Anthropic, and Mistral APIs — swap or weight providers with one config change, no downstream code changes required.
Applies to: providers exposing a compatible chat or embedding API. Model-specific features are normalised only where more than one provider supports them.