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Solution

Add AI to your existing product, without rewriting it

A focused engagement to ship one AI feature into your existing product safely, in eight to twelve weeks.

8 to 12 weeks for the first feature, with optional follow-on engagements for feature 2 and beyond.

You probably need this if

  • Customers asking for an AI feature you cannot ship yet
  • Competitors shipping AI features quickly
  • In-house team busy on the existing roadmap
  • No clear path from prototype to production AI

How we approach it

The first AI feature in a product is the hardest one to get right. Kiebot ships your first production AI feature in 8 to 12 weeks: one well-chosen use case, one production-quality build, one eval harness so you can keep improving it after we leave. The team that wrote the OpenClaw and HermisAgent runtimes inside Kiework and Open Agent.

  1. 1

    Pick the right use case

    Not every AI feature is a good idea. We help you pick the one that is most valuable to customers, lowest risk to ship, and most learnable from real usage.

  2. 2

    Build the eval set first

    Before any model call, we build the eval set: 50 to 200 examples that define success for this feature. We will refer back to it every week.

  3. 3

    Ship a thin slice

    A working AI feature behind a feature flag, in production, in 4 weeks. Real users, real feedback, real cost data.

  4. 4

    Harden

    Guardrails (input filters, output filters, cost caps, human-in-the-loop for irreversible actions). Observability for every model call.

  5. 5

    Hand it over

    Architecture notes, evals, runbooks, and a written plan for what to ship next.

What you should expect

  • One AI feature in production with real users in 8 to 12 weeks
  • Eval-driven development discipline your team can keep using
  • Clear cost model and guardrails before the bill scales

Related

Stacks to hire

Common industries

Frequently asked questions

Do we need a data team to add AI?+

Not for most product AI features. RAG plus prompt engineering on hosted models gets you a long way. Data science teams matter when you train models, not when you call them.

What about the LLM cost?+

Cost is part of the design. We pair the right model size to each step, semantic-cache the common cases, and put hard token caps in place. Most clients land at 30 to 60% of their initial cost estimate.

Which LLM provider do you recommend?+

We are LLM-agnostic. We pick OpenAI, Anthropic, Google, or open weights per use case. See /insights/the-llm-stack-2026 for our current shortlist.

Want to talk this through?

Twenty minutes on a call, no slide deck. We will tell you straight whether this engagement fits or what would.

Talk to Kiebot