Most agentic AI projects die between the demo and the second week of production traffic. We exist to ship the rest of the iceberg — the evals, the routing, the human-in-the-loop gates, the telemetry, the runbook, and the on-call. The part that turns "an interesting prompt" into a system your business runs on.
Frontier models change every quarter. Your moat is the orchestration, the evals, the fallback paths, and the institutional knowledge baked into the tool layer — not the model number on the API call.
Every agent we ship has a golden eval set the customer signs off on, regression runs on every change (model, prompt, tool, data), and a quality dashboard your team can read without us.
We map every external action to an approval class — auto, soft-confirm, hard-approval, or manual-only — based on reversibility, blast radius, and regulatory posture. Override paths are first-class, not a fallback.
If you can't replay a 3-month-old trace in under 60 seconds, you can't operate the agent. We ship telemetry on day one, not after the first incident.
Six disciplines we've refined across every agent we've shipped. Each one survives a model upgrade, a vendor swap, and a year of production traffic.
We build a golden set of 200–500 representative inputs per agent — sourced from your real tickets/transcripts/docs, anonymized, and reviewed by your SMEs. Each is scored against a customer-signed rubric (correctness, faithfulness, refusal-when-needed, action-safety, tone). Every PR — prompt, tool, model, retrieval index — runs the full suite. CI blocks merge below 95% pass-rate on the rubric the customer owns.
A typical agent makes 6–20 model calls per task. We route each turn by reasoning depth, latency SLA, and per-call cost ceiling — Opus / Sonnet 4.x / GPT-5 / Haiku / Gemini Flash in the same workflow, with auto-failover. Router decisions are logged with rationale; you can A/B route policies the same way you A/B prompts.
Every external write action is classified at design time: auto (read-only / fully reversible), soft-confirm (one-tap, can be undone in 24h), hard-approve (human signature + audit trail), or manual-only (agent prepares, human executes). Mapping is reviewed with your compliance team before a single tool gets wired up.
We instrument with OpenTelemetry-compatible tracing (Langfuse / Phoenix / Datadog LLM Observability, your call). Every trace captures the full plan, every tool call with inputs/outputs, the model variant, latency, token cost, and the human override if one happened. A 3-month-old incident replays in 60 seconds. Your team owns the dashboards on day one.
We maintain an internal catalog of agent failure modes (prompt-injection vectors, tool-loop patterns, hallucinated citations, role-leak, refusal-bypass, retrieval-poisoning, cost-runaway, stale-context, etc.). Every system ships with regression tests for every applicable mode, and a quarterly review against new modes that emerge in the field.
Bad traces from production (low-confidence outputs, escalations, customer corrections) are auto-clustered and surfaced weekly. The most informative get added to the golden eval set — with your SME's sign-off — so the system measurably improves and regressions get caught the moment they appear.
Eight layers. We pick the components per engagement (your cloud, your data residency, your model procurement) — but the contract between layers is always the same. That's how we hand off without leaving you stranded.
We don't do open-ended T&M. Every engagement is a fixed-fee SOW against a fixed outcome — the KPI we commit to in week 0. You know what you're getting, when, and at what price.
The hard problem wasn't the form — it was reading specialist notes and matching them to per-payer policy. We built a citation-grounded retrieval layer that returned the exact policy clause, then the agent composed the request with quoted evidence. Reviewers could re-verify in 90 seconds.
Analyst-grade investigations require evidence assembly across 10+ systems in under 2 seconds. We pre-fetched the high-signal context per transaction (device, velocity, ring-membership) and pushed it into the agent's plan so the LLM only reasoned over a curated brief — not the raw firehose.
Returns are a conversation, not a form. Our agent asks the right diagnostic question (fit, expectation, quality) and offers the right swap from live inventory — before defaulting to refund. Every conversation runs A/B against a hold-out where customers go straight to refund.
Optyzone is founder-operated by design. Shashi takes your discovery call and stays your point of contact. Bhanu owns the architecture and reviews every PR. You always know who's accountable.
Owns the Optyzone methodology, the reference architecture, and every agent that ships under our name. Two decades building data and AI systems for Fortune 500 healthcare, fintech, and retail teams — and shipping agent platforms before the category had a name. Reviews every PR; owns every architectural call.
Runs every discovery call, scopes every engagement, and stays close to the customer through go-live. Background in enterprise sales and partnerships across regulated industries — the person who makes sure the agent we build is the agent your business actually needs. Your single point of contact from first call to handoff.
Ship a chatbot when your problem is a workflow agent.
We turn down deals where a deterministic workflow with light LLM augmentation would serve you better — and tell you so on the discovery call.
Run on yesterday's model because we're cheaper that way.
Our routing benchmarks the current frontier monthly. If a new model meaningfully improves your eval at a cheaper price, we ship the migration as part of the retainer.
Disappear after handoff.
Two engineers carry pages for 30 days post-launch. A standing quarterly review for the life of the system, included.
Sell you data labeling, training compute, or our own SaaS.
We have no proprietary product, no labeling vendor kickback, no compute markup. Pure build engagement — your IP, your stack, your operating cost.
The next step is a 45-minute discovery call. We come prepared with a sketch of what we'd build for your team — and a candid view on whether you should even use us.