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Knackline

AI systems that hold up outside the demo.

Agents, production RAG, and enterprise AI platforms built for the failures demos hide.

Where demos break in production

Most AI pilots fail the same quiet ways. We design for those failure modes first.

  • Retrieval that looks fine in the pilot

    Chunking drift, stale indexes, and citation gaps show up only after real users ask messy questions.

  • Agents that loop or invent tools

    Without a harness, permissions, and evals, demos become ops incidents the moment traffic arrives.

  • Answers nobody can audit

    Leaders need traces, eval drift signals, and human gates before they trust the system with decisions.

Who this is for

  • Engineering leaders

    Shipping AI behind clear interfaces, not a folder of prompts.

  • Platform and data teams

    Making retrieval, metrics, and access control survive production.

  • Product owners of AI features

    Moving from a polished demo to something operators can run.

How we solve

A sequence we repeat until the AI system tells the truth again. Not a framework deck.

Whiteboard covered in systems architecture sketches
  1. Diagnose

    We start with how the AI system fails in the wild: retrieval misses, agent loops, silent tool errors, eval drift, and answers nobody can audit.

  2. Model

    Constraints become a map: data contracts, tool permissions, memory boundaries, harness topology, and the smallest path that proves the design.

  3. Build

    Agents, RAG pipelines, generative BI layers, and reviewers ship behind clear interfaces so teams can iterate without rewriting the substrate.

  4. Harden

    Observability, eval harnesses, human gates, and docs catch what demos hide. We leave the system operable by the people who inherit it.

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.

Reports

Deep writing on what breaks after the AI demo, and what to measure before you scale.

All reports

Craft & open source

Tools we extracted from real delivery pain, including AI-assisted analytics, and published so the next team does not start from zero.

Lived-in developer desk with terminal, notebook, and coffee
  • Netrasight

    Laravel, Vue

    AI-assisted analytics so teams can ask questions of their databases, an early signal of generative BI without standing up a separate stack first.

  • Vidur

    Nuxt, Nest

    Host career sites, shortlist candidates, run recruiting workflows, and integrate hiring plugins, built to scale without ceremony.

  • laravel-excel-to-x

    Laravel

    Convert Excel into JSON or collections with a small, predictable API, the kind of utility that disappears when it works.

  • KnackGit

    Rust

    CLI and macOS menu bar app for switching between multiple Git accounts without breaking your day.

  • laravel-deploy

    Laravel

    Shell-scripted Laravel deploys packaged so teams stop reinventing the same fragile release ritual.

  • UnMail

    Nuxt

    Unified transactional email API across providers, with room for custom drivers when the defaults are not enough.

Dim night desk with laptop and monitor glow, papers and books nearby

Since 2019

An engineering practice focused on AI agents, production RAG, enterprise AI, and generative intelligence layers.

Full company story
Founded
2019
Focus
Enterprise AI
Practice
Agents & RAG
Writing
Reports
  1. 2019

    Knackline founded as a hands-on engineering practice.

  2. 2021

    Strengthened technical leadership and delivery standards.

  3. 2022

    Open-source journey began: packages and tools meant to outlive any single client project.

Later milestones live on the About page.

Ready to talk production?

Tell us where the AI system is failing, or what you need to ship next. We reply when we can be useful.