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Knackline

Knackline

AI systems that hold up outside the demo.

Agents, production RAG, enterprise platforms, and observability for teams who inherit reliability after the demo. Built for the failures demos hide.

Tell us what's failingSee who this is for

Opens a short note to Knackline. Not a product demo or signup.

Built for people who inherit the system

If you own reliability after the demo, you are the buyer we write for. Engineering leaders, platform and data teams, and AI product owners. Demo shoppers and storefront pitches are not.

  • Engineering leaders

    You own reliability after the vendor leaves. You need AI behind clear interfaces, eval gates, and an operable handoff, not a folder of prompts.

  • Platform and data teams

    You live retrieval, metrics, access control, and routing daily. You need contracts and traces that survive production traffic.

  • Product owners of AI features

    You have a polished demo and a deadline. You need something operators can run, with clear L1 through L3 ownership when it fails.

Where demos break in production

These are the buying triggers we hear from engineering and platform teams who inherit the system. If you recognize one, you are in the right place.

  • Retrieval misses

    Chunking drift, stale indexes, and citation gaps show up only after real users ask messy questions. Platform and data teams feel this first.

  • Agent loops

    Tool calls succeed while the task never finishes. Retries burn tokens and side effects. Engineering leaders inherit the pager.

  • Tool errors

    Invented tools, permission denials, and silent failures without a taxonomy operators can act on.

  • Evaluation drift

    Prompts, indexes, and models move. Metrics stay green while task success collapses. Nobody can prove what broke.

  • Missing auditability

    Leaders cannot see traces, citations, or who approved an irreversible action. L1 through L3 ownership is unclear.

What are you trying to make reliable?

Pick the failure mode your team owns. We route you to the matching production path, not a generic capability brochure.

How Knackline works

Diagnose, model, build, harden. Each stage leaves an artifact the inheriting team can own when something breaks at 2am, with clear L1 through L3 ownership.

Diagnose

Failure inventory and eval baseline: retrieval misses, agent loops, silent tool errors, and answers nobody can audit. We map who gets paged for L1 through L3 before we decorate the demo path.

Output for the inheriting team: A ranked failure map, accountability notes, and the smallest test set that proves the problem.

Workflow in detail

The same loop we use on delivery engagements, drawn as the paths operators inherit.

Each pass starts from a real failure mode and ends with something operators can run.

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Prefer the written deep dives? Read the reports.

What the inheriting team keeps

Each engagement ends with artifacts platform and engineering owners can run. Not a slide deck of model magic.

  • Failure map

    A ranked inventory of what breaks first, who gets paged, and the smallest eval set that proves it.

  • Owned interfaces

    Contracts for data, tools, memory, and harness topology operators can inherit.

  • Working path

    Agents, RAG, or BI behind clear interfaces. Not a notebook that only works on stage.

  • Operable handoff

    Traces, gates, and runbooks so L1 through L3 know who gets paged when it fails.

Evidence

Technical specificity is the proof engineering and platform teams trust. Reports and patterns we publish when we solve the same failure twice for teams who inherit the system.

Selected reports

All reports

Published patterns

If we solve a problem repeatedly, we turn it into a reusable pattern. If useful, we publish it.

Before you write

Quick answers for buyers who need to know whether this conversation is worth their time.

Prefer depth first? Read the reports.

Engineering leaders, platform and data teams, and product owners of AI features who inherit the system after the demo. If you own reliability when the vendor leaves, you are the buyer we write for.

Where is it failing?

Share the failure mode, the constraint that hurts, and who inherits the system. We reply when we can be useful to engineering leaders, platform teams, or AI product owners.

Tell us what's failingBrowse failure modes

A short note is enough. No pitch deck required.