Authoricy · AI Labs

Your pilot didn’t stall because
the model wasn’t good enough.

It stalled because nothing closed the loop. Something produced output, nobody checked it, nothing was recorded, and nothing changed as a result. We build the systems that watch their own work and correct themselves, inside your company, on your stack. You own the infrastructure and the code when we leave.

Book the diagnosticSee a system we run
04phases
90days to production
100%source owned by you

The problem

Most companies have already tried. There are subscriptions, a few automations, and a pilot that showed promise and then went quiet. What is missing is rarely the model. It is the recording, the checking and the correcting that would have turned the demo into a system.
95%of generative AI pilots deliver no measurable financial return, across 150 interviews, a 350-employee survey and 300 public deployments. (MIT NANDA, 2025)
12%of enterprise AI pilots reach production at all. (IDC and MIT)
40%of agentic AI projects are expected to be cancelled by 2027. (Gartner)

Proof

We would rather show you our own system than describe someone else’s.

Authoricy sells a second thing: a system that researches, writes and publishes articles to customer websites every day, with nobody in the path. It is the same architecture we build inside client companies, and it is the one we can open up completely.

01

Sense

The system reads the state it is meant to act on, and the record of what it already did.

02

Decide

It picks an action against an explicit target, and checks the conditions that would make it wrong.

03

Act

It executes with nobody in the path.

04

Record

The run leaves a durable artifact in the database, not a log line: what happened, why, and what it cost.

05

Adjust

Tomorrow reads that record, and decides differently because of it.

Two decisions in there carry the weight, and neither concerns the model. The ledger has a uniqueness constraint enforced by the database rather than by application code, so a cron that fires twice or a container that restarts mid-run cannot publish the same article to a customer’s live site again. And the quality score is a publishing condition rather than a dashboard metric: below the bar, the article is held as a draft and the reason is recorded. Some months that means a customer gets twenty-six articles instead of thirty, along with an explanation of the four.

What went wrong

It failed three times in production. None of them produced an error.

Two of the three reported success. We found all three by querying the live database and comparing what was there against what the code assumed, which is the only method that works when the failure mode is silence.

01

It had nothing to write about

The component proposing content topics inserted them with empty keyword arrays, waiting on a configuration step nobody had built. Every topic reached the scheduler with no subject attached. The scheduler looked, found nothing, and skipped. It did that correctly, and without complaint, every day. We checked production and found all four topics empty, including the three that were live. The pipeline was structurally complete and had never been capable of producing a single article.

02

The ledger did not exist

We deployed the code that writes to the run ledger before the migration that creates the table. Both safety guards read that table: has this run today, and how many have published this month. With the table unreadable, both queries came back empty and both guards answered go ahead. A missing safety mechanism did not stop the system. It would have made it publish the same article to live customer sites over and over, while reporting success.

03

Abandoned checkouts got full access

Our billing code mapped every subscription status that was not explicitly active or past due onto active. Stripe creates subscriptions in an incomplete state before payment clears, so every signup passed through that branch. Anyone who opened the payment form and walked away was provisioned a paid plan with a month of credits. Cancelled subscriptions read as active too.

None of these were model failures. No hallucination, no bad prompt, no capability limit. All three were loops that looked closed and were open. That is the failure signature of AI systems running without supervision, and it is why the 95% number is not really a statement about AI quality.

We publish this because the failures are the qualification. Anyone can show you an architecture diagram. The useful question is what happens when the diagram is wrong, and we can answer it with three specific cases from our own production database. The full teardown is on the blog, including the fixes.

The method

Ninety days, four phases, one working system.

012 weeks

Diagnostic

We map how work actually moves through your company: the places where a person is the integration between two systems, where a decision waits three days for the right people to be in a room, where the same data gets entered twice. Then we size the three to five interventions that would pay for themselves, against your real cost structure rather than a vendor benchmark.

DeliverablePrioritised impact map with ROI sizing and the reasoning behind the order.
022 weeks

Blueprint

We design the architecture. What gets built, what gets integrated from tools you already pay for, how data moves between them, and what each system records so the next decision can read it. You get a plan with milestones, named owners and a delivery calendar you can hold us to.

DeliverableTechnical architecture and a 90-day delivery plan.
036–10 weeks

Build

We build and deploy. Agents, workflows, data pipelines and internal tools, all of it to production standard, with the recording and the idempotency guarantees in from the first commit rather than added once something goes wrong. Source and documentation transfer as the work happens.

DeliverableDeployed systems with full source, documentation, and a handover session.
044 weeks

Handover

We train everyone who touches the systems, in live sessions built around their actual job rather than click-through documentation. Each function gets a written playbook. We stay embedded for thirty days while your team runs it. The engagement is over when nobody needs to ask us anything.

DeliverableRole playbooks, governance framework, and a team operating the systems.

What you own

You keep what we build.

When the engagement ends you have working infrastructure and the knowledge to run it. Source code, documentation, playbooks, and engineers on your side who understand every part of it. We are not trying to become a dependency.

Is this for you

We work with a specific type of company.

This is for

  • 50 to 500 people, with operations complex enough to be worth automating
  • Teams at the ceiling of what point tools can do for them
  • CEOs and COOs who want something running, not a slide deck
  • Logistics, finance, sales operations, content production, customer service

This is not

  • Early-stage companies still finding product-market fit
  • Anyone shopping for a strategy report
  • Teams who cannot give the process real internal time
  • Companies who want to license software rather than own it

Common questions

About the engagement

What do you actually deliver?

Running software. Deployed systems, full source code, technical documentation, a playbook per function, and a team trained to operate the lot. Everything transfers as the build happens rather than in a handover at the end.

Who owns the systems afterwards?

You do, outright. No licence, no subscription, no lock-in. The systems are built to run without us, which is the point of the last four weeks.

How long does it take?

Around 90 days: two-week diagnostic, two-week blueprint, six to ten week build, four weeks of training and embedded support. The diagnostic can be booked on its own before you commit to the rest.

What does it cost?

Engagements are scoped and priced individually, because the work is never the same twice. The diagnostic is fixed scope and quoted before it starts. You get the number on the first call rather than after three.

Why not just buy AI software?

Bought software is built for the average of its market, and you cannot change what it records or when it refuses to act. Those two things separate a system that improves from one that decays without telling you. Custom systems are built around how your company already works, and your team extends them once we are gone.

How do you start?

Book the diagnostic. Two weeks, fixed scope. We map your operations, identify the three to five interventions worth making, and hand you an impact map with ROI sizing. You keep it whether or not you go further.

Start here

Begin with the diagnostic.

Two weeks, fixed scope, quoted before it starts. You leave with a prioritised impact map and a straight answer about where a closed-loop system would pay for itself in your business. That holds whether or not you go any further with us.

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