42%
of companies scrapped most of their AI initiatives in 2025.
Up from 17% the year before. Not failing — quitting. — S&P Global Market Intelligence, 2025
We are the AI agency that plans the labyrinth before building the wings. Diagnostic first. Build second. Measured always — so your project is one of the ones that lands.
Thirty minutes. No deck. If we are wrong for you, we say so on the call.
Diagnostic before build
Every engagement starts with six questions, not a demo.
Built for operators
Mid-market teams and scaling startups, 50–500 people.
Handover included
Runbooks, instrumentation, and a team that can run it without us.
Why AI projects fail
The fall is always the same.
Enterprise teams spent $37 billion on generative AI in 2025 — while 46% of proofs-of-concept were scrapped before reaching production, and 95% of pilots showed no measurable return. The pattern behind the wreckage barely varies:
No diagnostic
Most projects start with a tool and a hope. Not a diagnostic. Not a measurement plan. The build begins before anyone asks where AI actually earns its keep.
No number
Nobody agreed what 'working' means before the work started. Without a metric it is judged on, every AI system is six months from being someone's regret.
No handover
The ones that ship rarely survive contact with month three. No runbook, no owner, no instrumentation — so they stall the day the consultants leave.
None of these are technology failures. They are planning failures. Which is good news — planning is a solvable problem.
Sources: Menlo Ventures — The State of Generative AI in the Enterprise (2025) · S&P Global Market Intelligence (2025) · MIT Project NANDA — The GenAI Divide: State of AI in Business (2025)
The Daedalus diagnostic
We plan the labyrinth before we build the wings.
Every engagement begins with the same six questions. We publish them because the answers matter more than the secret — and because you can tell a lot about an agency by what it asks before it quotes.
- 01
Where does an hour of human time cost you the most?
AI earns its keep where labour is expensive and repetitive. We find that spot before choosing any tool.
- 02
What data do you actually have — and can we reach it?
Stalled projects usually die at the integration, not the model. We score data readiness before a dollar is committed.
- 03
What does 'working' mean, in a number you already track?
If success is not a metric you measure today, the project cannot be judged — only abandoned.
- 04
What breaks if the system is wrong five per cent of the time?
Error tolerance decides architecture. A drafting tool and an autonomous decision-maker are different machines.
- 05
Who owns this after we leave?
A system without an owner is a countdown. We name the owner — and write their runbook — before we build.
- 06
What is the smallest version that pays for itself?
The first release should earn its budget back. Scope grows after proof, not before it.
The diagnostic produces a feasibility score and a sequenced roadmap with rollback points. Sometimes the honest answer is 'wait' or 'don't'. You get that answer too.
What we build
Plan. Build. Ship.
Fourteen concrete capabilities across the three disciplines that decide whether an AI project ships or stalls.
01 — Plan
Diagnostic interview
The six questions that uncover where AI actually earns its keep — before a single tool is chosen.
Engagement roadmap
A sequenced build order with milestones, measurement plan, and rollback points. Not a deck.
Feasibility scoring
Data readiness, workflow clarity, integration cost. A score that says ship / wait / don't.
02 — Build
Custom agents
Task-scoped agents wired to your operations — not chat toys. Each one owns one outcome.
AI-powered workflows
Replace the manual steps that pile up between systems. Orchestrated, logged, reversible.
Lead-gen systems
Intent capture, qualification, and hand-off — tuned to the prospect you actually want.
Integrations
Your CRM, your inbox, your calendar, your data warehouse. Plug in; no rip-and-replace.
Content operations
Evergreen pipelines that produce on-brand assets on schedule, with a human still in the loop.
Reputation systems
Review capture, response automation, and signal-to-noise triage for your customer voice.
03 — Ship
Instrumentation
Every build ships with the metrics it is judged on. Pipeline, throughput, cost-to-serve.
CRM wiring
The system of record stays authoritative. No shadow spreadsheet, no drifting state.
Runbooks and handover
A written operations manual for the team inheriting the build. Not tribal knowledge.
Hosting and reliability
Deployed to your infrastructure or ours. Monitored, alerting, and versioned.
Scheduling and bookings
Qualified prospects end up on a calendar without a human in the scheduling loop.
How we work
Five stages. No leaps of faith.
- 01
Discover
The diagnostic interview. Your operations, your data, your numbers — before any tool talk.
- 02
Plan
Feasibility score, sequenced roadmap, measurement plan, rollback points. You see the whole flight path.
- 03
Build
Task-scoped systems wired to your operations. Each one owns one outcome and ships with its own metrics.
- 04
Integrate
Your CRM, your inbox, your calendar, your warehouse. The system of record stays authoritative.
- 05
Optimise
Throughput, cost-to-serve, and hand-over measured monthly. The build earns its keep or we change it.
Why the name
Named after the one who landed.
Daedalus built the wings — and planned the labyrinth before them. His son flew high, fast, and briefly. The difference was never the wings. It was the planning.
The story experience is coming
Before you ask
The questions operators actually ask.
We already tried AI and it stalled. Why would this be different?
Because we start where your last project should have: a diagnostic that scores data readiness, integration cost, and workflow clarity before anything is built. Most stalled projects fail the diagnostic — which is exactly the point of running it first. Sometimes the answer is 'wait'; you will hear that from us, not discover it in month six.
What does an engagement cost?
The diagnostic is scoped and priced as its own fixed engagement, so you are never buying a build you have not seen the plan for. Build pricing comes out of the roadmap — sequenced so the smallest version pays for itself before scope grows.
How long until something is live?
The diagnostic takes days, not months. First production release is typically measured in weeks — because we ship the smallest version that earns its budget back, not a platform.
What happens to our data?
Your data stays in your systems; we integrate rather than extract. Where a build touches sensitive data, the boundaries are written into the plan before the build — including what never leaves your infrastructure.
Do we end up dependent on you?
The opposite is the deliverable. Every build ships with instrumentation, a runbook, and a named owner on your team. We measure hand-over as an outcome — a system only we can run is a system we consider unfinished.
Why 'Cyber' — are you a security firm?
The name reads through the Greek: kybernetes — the steersman. We steer AI systems into production. Security discipline comes with the territory, but the business is AI systems, workflows, and lead generation.
Start the conversation
Bring us the problem before the tool.
A thirty-minute strategy call. No proposal templates. No deck of logos. We find where AI earns its keep in your operation — and we tell you plainly if we are not the right partner.
- Thirty minutes, on your calendar
- The six questions, applied to your operation
- A straight answer — including 'wait' or 'don't'