Codiste Put a number on the gap

Measure the agent against what actually happened.

Codiste takes AI agents from architecture through to production traffic for venture studios, funds and the founders they back, in FinTech, RegTech, PropTech and MarTech. Every claim we make about one is a claim about the distance between what it predicted and what turned out to be the case.

Put a number on the gap Ask what we would measure

Against a reference

An agent on its own is not a result. It becomes one the moment there is something to hold it against, and that comparison is the part most teams never build.

The face of an analogue dial indicator on its stand, lit from the left against a dark background, its needle resting off zero
A needle resting off zero is not a fault. It is the distance nobody had measured yet.
150+Systems shipped
5+Enterprise-grade logos
1Product we run ourselves
18Days to live traffic on our quickest voice deployment

Each of these is a reading. None of them means anything without the thing it is measured against, which is why they are never quoted here on their own.

Eight disciplines held against one outcome.

Split them between five vendors and each one is measured against its own contract instead. That is how a system passes every review and still fails the only one that counts.

Scope a first release

A demonstration is measured against itself.

Whoever wrote the prompt decides what a good answer looks like, and the agent is compared with that. Real traffic supplies its own reference, and it is a harsher one. The model goes on answering against a standard nobody set, confidently, until somebody outside the building notices the difference.

Diligence is the same comparison run by a stranger. An investor weighs what you say the system does against what you can show it did. Where nothing was measured, the gap between those two is assumed to be large, and the assumption is rarely generous.

We are measured on speed, leverage and risk.

Speed

Weeks against quarters. Our quickest voice deployment was carrying real requests on day 18.

Leverage

Senior engineers against a learning curve you would otherwise fund yourself.

Risk

Guardrails, evaluation and hallucination control designed in, against the cost of retrofitting them.

Three competencies, each answerable to the same measure.

Artificial Intelligence

Architecture, guardrails and evaluation drawn up together, so that what the system does can be checked against what it was supposed to do.

Blockchain Innovation

Decentralised applications weighed for scale before they are written, on secure and transparent ledgers that hold data intact across enterprise ecosystems. Supply chain transparency. Tokenisation.

Machine Learning

Forecasting through to hyper-personalisation, weighing an archive that costs you against one that returns something. Predictive analytics. Computer vision.

Four steps, and each one narrows the gap.

The same route whether the work is a seed-stage voice product or a Series C platform fitting agents into a core system.

  1. 01

    Discover

    Product, data and every decision the agent will own are written down in the opening days, together with what the result will be compared against.

  2. 02

    Prototype

    Your team gets something they can operate early, so the judgement is behaviour against expectation rather than a description against a hope.

  3. 03

    Launch

    It goes out hardened, guardrails enforced, and the audit trail writing from the first request rather than the first complaint.

  4. 04

    Iterate

    Logged interactions are read against the previous revision. Tuning and retraining carry on as long as the two keep diverging.

Four agents, and what each one replaced.

Each one replaced something that was already there. The two lines are the same job, before and after.

  • 01

    Propizone CRM Voice AI

    BeforeA portal enquiry at eleven at night reached voicemail and waited for the morning.

    NowIt is answered in eight seconds and the lead is qualified before the caller has closed the listing.

  • 02

    CandiPro ATS Voice AI

    BeforeFive hundred applicants meant five hundred screening calls queued behind one recruiter.

    NowThe agent takes the hiring manager's interview itself and hands the shortlist back ranked.

  • 03

    Neo-Bank Voice AI

    BeforeAccount questions and transfers were two separate journeys, one of them a phone queue.

    NowA voice layer inside the US neo-bank app answers and then moves the money, confirming every transfer before it clears.

  • 04

    ReachOut Voice AI

    BeforeA form submission waited on whoever next checked the inbox.

    NowReachOut's agents ring back within seconds, while the person still wants the conversation.

One arrangement, measured the same way at every company you back.

Rates, priority access and delivery frameworks are agreed once at fund level and hold wherever your money sits. We learn the thesis beside your partners, so a founder skips the vendor search and comes to a first call already briefed.

  • FinTech
  • RegTech
  • PropTech
  • MarTech
  • SaaS
  • AdTech
  • SportsTech

Eight frameworks, and the system is already held against them.

Data residency, PII handling, audit trails and explainability are fixed in the architecture. A review compares a system with a standard and finds no distance between the two.

What a fund gets when the gap is measured rather than assumed.

01

Studio velocity

Engineers who already know where production agents diverge from their demo, so the opening weeks go on the build rather than on finding out.

02

Cross-stack fluency

A portfolio runs on many stacks and so do we: voice, LLM, agentic, retrieval, edge, and whatever legacy system is still carrying the load.

03

Reliability as discipline

Guardrails, evaluation and hallucination control are structural here, weighed against the cost of adding them in the last fortnight.

04

Investor-grade delivery

A round, a runway and a reputation are all being weighed at once. The work is done as though our name sat beside yours, because it does.

Tell us where the gap is.

Describe the agent, the system it lives inside, and what you believe it does against what you can currently show. An engineer replies with an approach, an effort range, and the first thing worth measuring.