Enterprise · Bespoke AI engineering

Enterprise · Custom AI agent engineering

Off-the-shelf doesn’t fit. We engineer one that does.

Bespoke AI agents engineered around your data, your stack, and your edge cases — reasoning loop, custom tool calls, eval suite, and production deploy with guardrails and observability you can defend in a review.

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Bespoke AI engineering

Agents that survive contact with your edge cases.

Most off-the-shelf agents work on demo data and fall over the first time they hit yours. We engineer the agent to your workflow — your tools, your knowledge base, your error modes — and ship it through an eval-driven loop that measures real outcomes, not vibes.

Every engagement starts with a discovery workshop, produces a signed-off spec, and ends with a production deploy your team fully owns. No vendor lock-in, no opaque model contract — the prompts, tools, and policies live in git like real code.

  • Reasoning loop tuned per workflow — not a generic agent template
  • RAG over your data with citations, not hallucinations
  • Custom tools that read and write the systems you already run
  • Eval suite gates every prompt change in CI
  • Guardrails, refusals, and human escalation routes built in
  • Observability you can actually read — every conversation logged
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agent.run() · session-7d4a
Trace · live

Goal

"Reschedule my appointment to Friday afternoon"

🧠

reasoning

Customer wants to reschedule → check Friday afternoon availability

🔧

tool · calendar.query

calendar.query('Friday PM', user_id)

← 3 slots: 1:00, 2:30, 4:00 PM

💬

reply

"We have 1pm, 2:30, or 4pm Friday. Which works?"

← user: "2:30 please"

🔧

tool · calendar.book

calendar.book('Fri 2:30 PM', user_id)

← booking #4827 confirmed

✓ resolved · 1.2 s · 4 tool calls · 0 escalations

Eval pass

Engagement model

Four phases. No mystery meat.

  1. 01

    Discovery + spec

    Workshop your workflow, define success metrics, draft the agent contract. You see the signed-off spec before any code lands.

  2. 02

    Scoped prototype

    Working agent on real (or anonymized) data, end-to-end. Real conversations, real tool calls, real evals — by week 3.

  3. 03

    Eval + harden

    Purpose-built eval suite, guardrails, escalation routes, and observability — everything you need to defend it in production review.

  4. 04

    Ship + transfer

    Production deploy, runbook, knowledge transfer. Your team owns the codebase + extensions. AIKoders retains no licensing claim.

By the numbers

Engineered to clear production review.

  • 6–12 wk

    Avg engagement

    Kickoff to production deploy.

  • 100%

    Eval-gated deploys

    Prompt or tool changes pass the eval suite or they don’t ship.

  • Yours

    Code ownership

    Repo, prompts, tools, policies — all in your git org.

  • Any

    Model backend

    OpenAI, Anthropic, open-source, or private — swappable.

What you get

A complete agent stack, built around your business.

  • Scoped to your outcome

    We start with measurable success criteria, not a feature list. The architecture flows backwards from there.

  • RAG over your knowledge

    Docs, database, APIs, tools — indexed and grounded so every answer cites a source.

  • Custom tool calls

    Read + write your CRMs, ERPs, warehouses, and internal APIs. No "we connect via Zapier" cosplay.

  • Eval suite

    Purpose-built evals gate every prompt and tool change. Regressions caught in CI, not in customer reviews.

  • Guardrails

    Refusal rules, PII filters, escalation routes, and rate limits in the agent runtime from day one.

  • Observable

    Every conversation, tool call, and decision logged and scoreable — drift surfaces before customers see it.

  • Version-controlled

    Prompts, tools, policies live in git. Promote through CI; rollback in seconds.

  • Stack-agnostic

    OpenAI, Anthropic, open-source, or your private model — same agent contract, swappable backend.

  • Owned by your team

    Knowledge transfer + runbook + ongoing support. You can extend it without us.

Built for these teams

Where bespoke beats one-size-fits-all.

  • Operations + Logistics

    Dispatch, routing, exception triage agents

    Ops desks scale 5× without proportional headcount; SLAs hold under volume spikes.

  • Healthcare

    Patient intake, scheduling, clinical triage

    HIPAA-safe deploys with audit trails that pass clinical operations review.

  • Financial Services

    KYC review, document analysis, compliance

    Manual-review queues shrink by 70%+ while keeping human-in-the-loop on the edge cases.

  • B2B SaaS

    Onboarding, customer success, lead qualification

    High-touch onboarding for low-touch ACV — agents handle setup, humans handle expansion.

  • Legal + Compliance

    Contract review, clause extraction, redlines

    Associate-level grunt work compressed from days to hours, with citations attached.

  • Engineering Teams

    Internal copilots, code review, runbook execution

    On-call gets a teammate who already read the docs and the post-mortems.

Common questions

What teams ask before they commission a custom agent.

  • How is a custom agent different from an off-the-shelf chatbot?
    Off-the-shelf agents (Intercom AI, Drift, generic GPT wrappers) ship with a fixed reasoning loop and limited tool access — you adapt to their conventions. A custom agent inverts that: we engineer the reasoning loop, the tool calls, the refusal policy, the eval framework, and the deployment around your workflow, your data, and your edge cases. The result is an agent that survives prod, not a demo that breaks on the first real customer.
  • What does the engagement timeline look like?
    Typical timeline is 6 to 12 weeks from kickoff to production. Weeks 1–2 are discovery + signed-off spec. Weeks 3–6 are a working prototype on real data. Weeks 7–9 are eval suite, guardrails, observability instrumentation, and production hardening. Weeks 10–12 are production deploy + knowledge transfer to your team.
  • How do we know the agent actually works before going live?
    Every engagement ships with a purpose-built eval suite — graded test cases on real conversations, refusal-rate tracking, hallucination detection, and CI gates that block prompt or tool changes from going live until they pass. You see eval scores at every demo; production deploy only happens after they're green.
  • Who owns the IP at the end?
    You do. Prompts, tools, policies, eval cases, and the integration code all belong to your team — committed to your git org, deployed from your CI. AIKoders retains no licensing claim on the agent itself; we keep the proprietary engineering primitives (eval framework, RAG abstractions) but those are the scaffolding, not the agent.
  • Can we run the model in our own VPC or on-prem?
    Yes. Custom agents can deploy in your AWS, GCP, or Azure VPC, or fully on-prem, with private LLM hosting (Llama, Mistral, Azure OpenAI behind your firewall). PII redaction at the prompt boundary, audit logs, RBAC + SSO — wired to your existing identity provider. See our Secure AI Deployment service for the full compliance stack.
  • What happens after we ship?
    You can take it from there — full code ownership and a runbook means your team can extend without us. Or we can stay on for ongoing telemetry review, eval updates, and feature iteration on a smaller monthly engagement. Most clients pick the second option for the first 6 months, then internalize.

Got a use case off-the-shelf can’t hit?

We build AI agents that actually ship.

Book a discovery call. We'll review your workflow, your stack, and the failure modes you need covered, then come back with a scoped engagement plan.

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