Managed Intelligence Provider

Your business runs on information.
We make it work.

An MSP looks after your devices. We look after the work those devices are doing — finding where your process is losing hours, applying AI to the work itself, then running it as a managed service. Measured in hours returned, not tickets closed.

4 weeks from first process mapped to something live in production
See how it works
Your data stays in your environment · Every decision traceable · No lock-in
Process health — example Illustrative
Processes live
6
Straight through
91%
In review
4
Invoice intake · 340 this week94% auto
Quote drafting · 18 in flightdrafting
Supplier onboarding · 4 exceptionsneeds a human
Month-end reconciliationcomplete
Knowledge retrieval · 212 askedall sourced

An illustration of what a live portal shows — not a client system, and not results we are claiming.

4wks
To your first live process
Human
Approval on consequential calls
100%
Decisions traced to a source
30days
Notice to leave, any time
Our Why

Most businesses aren't short of software. They're short of joined-up information.

You have the systems. You have the data. What you don't have is the thing that makes them talk to each other — so people do it by hand, and nobody calls that a problem because it has always been done that way.

Work that is really just re-typing

Information arrives as a PDF, an email or a form, and a person keys it into the system that needed it. It is the most expensive thing most businesses do without noticing.

Answers that take three days and four people

The question is simple and the data exists. Assembling it means chasing whoever owns each spreadsheet — so most questions never get asked twice.

An AI pilot that never left the pilot

A demo that impressed the room and changed nothing, because nobody owned making it part of how the work actually gets done — or proving it was right.

Process that lives in one person's head

It works because Julie knows the exceptions. When Julie is on leave it doesn't, and when Julie leaves, the process leaves with her.

What we do

Four disciplines, applied to one problem

Not four products you pick from. They are the sequence — you cannot automate a process you have not measured, and you should not run one you cannot audit.

Process intelligence

Before anything is built, we find out where the time actually goes — by watching the real work, not reading the procedure document.

  • Shadowing the process as it is really run
  • Volume, cycle time and rework, measured
  • The exceptions nobody documented
  • A costed case, before you commit

AI applied to the work

Models built into the process itself — reading, extracting, drafting, classifying and triaging — rather than a chat window sitting beside it.

  • Document understanding and extraction
  • Drafting and summarization in your voice
  • Classification, routing and triage
  • Retrieval that cites the source document

Information architecture

AI is only as good as what it can see. We get the data into one governed place so answers come from a source of truth, not somebody's export.

  • Integration across the systems you run
  • One governed model, owned by you
  • Permissions that follow your Entra groups
  • Reporting that stops being a monthly chore

Managed operations

We run what we build. Somebody is accountable when a model drifts, a supplier changes their invoice layout, or an edge case turns up at 2am.

  • Monitoring, accuracy tracking and drift alerts
  • An exception queue with real people behind it
  • Audit trail on every automated decision
  • A written outcome review every month
How it works

One process at a time, proven before the next

No transformation program. No eighteen-month roadmap. We take the process that hurts most, make it measurably better, and only then talk about the second one.

01

Map

Two weeks with the people who actually do the work. We follow a real case end to end, count what it costs in hours and rework, and come back with a costed case for the one or two places AI genuinely helps — and the places it doesn't.

02

Build

The smallest useful slice goes into production in weeks, inside your own environment. It runs alongside the humans first, so you can see what it would have decided before it decides anything. Anything consequential keeps a person in the loop.

03

Run and prove

We operate it: monitoring, exception handling, accuracy tracking and the fixes when a format changes upstream. Every month you get the numbers — volume, straight-through rate, hours returned and where it got things wrong.

The platform

AI you can hold to account

The reason most AI never gets near a real process is that nobody can answer "why did it do that?" Ours can. Every automated decision is recorded with what it saw, what it concluded, how sure it was, and who signed it off.

Every decision, traceable

The source document, the extracted values, the model's confidence and the person who approved it — kept together, and exportable when an auditor asks.

An exception queue, not a black box

When the model isn't confident it refuses to guess and routes the case to a person — with the reason it stopped. Those refusals are what we tune on.

A scorecard per process

Volume, straight-through rate, hours returned and accuracy over time. If a number is going the wrong way you see it before we explain it.

It runs token free on your machine

The models run on your own hardware, so there is no per-token meter on every document you process. Your data never leaves your environment, and we are not a destination your information gets copied to.

portal.kultiv.ai/processes
Invoice intake
Illustrative
This week
340
Auto
94%
Hrs back
28
INV-8841Matched to PO-2207 · 3 lines0.99
INV-8842Posted to Finance · no review0.97
INV-8843New layout — sent for review0.61

Illustrative — the layout is real, the invoices and figures are made up to show the shape of it.

We would rather return you an hour than sell you a license. If it is in your process, it is our problem.

2 wks
Discovery to costed case
4 wks
First process in production
Monthly
Outcome review, in writing
30 days
Notice to leave, any time
Who we serve

Built for the overlooked ones

Too small for the consultancies to send their good people to. Too document-heavy to keep doing it by hand.

The paperwork-heavy one

Invoices, forms, claims, applications, timesheets. Volume is growing and the only lever anyone has offered you is hiring another person to key it in.

High document volumeManual intake

The regulated one

Healthcare, finance, legal, government supply chain. You cannot paste client information into a public chatbot, and every decision has to be defensible months later.

Auditable AIData residencyISO 27001

The data-rich, insight-poor one

A dozen systems, none of which agree. Every board question becomes a two-week project and the answer arrives after the decision was needed.

Many systemsNo single view
Engagement

Priced per process. Quoted after we've seen it.

We won't put a number on this page, because anyone who quotes your process before watching it is guessing. Discovery is fixed-fee and it ends with a real number — including when the honest answer is that the process isn't worth automating yet.

HOW WE WORK TOGETHER
The engagement

A fixed-fee discovery, a fixed-scope build, then a monthly fee for each process we run for you. No per-seat licensing, no charge for the people who merely benefit from it.

  • Fixed-fee discovery — you keep the findings either way
  • Fixed-scope build, agreed in writing before it starts
  • Monthly managed operations, per live process
  • Model and infrastructure costs passed through at cost
  • Built in your environment — it is yours if we part ways
  • Thirty days' notice, documentation handed over
What the first months look like
Week 0Discovery call — free
Weeks 1–2Map and measure
End of week 2Costed case
Weeks 3–6Build the first slice
Week 6Live, shadowing humans
Month 2 onwardManaged

The discovery call costs nothing and takes about forty minutes. Bring the process that annoys you most — you will leave it knowing whether this is worth your time, including if the answer is no.

Use cases

Where we usually start

These are the processes that come up most often. They share a shape: high volume, information trapped in documents, and rules a person applies the same way every time.

Invoice & PO intake

Invoices arrive in a dozen layouts. They get read, matched against the purchase order, and posted — with anything ambiguous held back for a person rather than guessed at.

How this runs

Quote & proposal drafting

A first draft assembled from your price book, your past wins and the client's own brief, so your people edit and send rather than start from an empty document.

Claims & case triage

Inbound cases read, categorized, prioritized and routed to the right queue with the relevant history already attached, instead of sitting in a shared inbox until someone sorts them.

Client & supplier onboarding

Forms and certificates checked for completeness and expiry, data written into the systems that need it, and the chase-up handled — so onboarding stops being a fortnight of email.

Knowledge retrieval

Staff ask a question in plain language and get an answer drawn from your own contracts, policies and history — with a citation, so it can be checked rather than trusted.

Reporting & reconciliation

The monthly pack assembled from source systems rather than rebuilt by hand, with the variances explained and the exceptions flagged before anyone opens the file.

Advisory board

The people we take advice from

A small board we bring hard problems to — on where this kind of work actually holds up, and where it doesn't.

Jodi Blomberg

Jodi Blomberg

Advisory board

Experienced AI/ML executive with broad track record of creating products that embed data and machine learning to create automation, drive revenue and increase growth. Expert in translation between stakeholders and data scientists.

Jodi brings decades of experience in the data science and AI implementation world, and we are honored to have her on the team.

Justin Williams

Justin Williams

Advisory board

A career arc that started writing assembly code for Motorola chips, doing embedded and IoT work, moved through DevOps and platform engineering, into data science, and — since 2016 — into applied AI, including patented IP. Believes we’re all pioneers in the AI transition, and what we pay attention to matters in shaping the future. A craftsman at heart, hoping for a future that’s collaborative, not isolating — technology with heart.

Justin is the future of AI and we are excited to have him join us.

Questions

The things people actually ask

A managed service provider takes responsibility for your technology. A managed intelligence provider takes responsibility for what that technology is supposed to be producing — the information your business runs on, and the processes that move it around. We find where those processes lose time, apply AI to the work itself, and then keep running it. The word "managed" is the important one: we are not handing you a tool and leaving.

A consultancy leaves you a recommendation and an invoice. We stay on the hook for the thing running in production — the monitoring, the exceptions, the supplier who changed their invoice template last Tuesday. That is also why we price per process rather than per day: if it takes us longer than we thought, that is our problem, not a variation order.

No. We build inside the platform of your choice using enterprise model endpoints that carry no-training commitments, so your documents stay under your retention, residency and access rules. We will put that in the agreement, and we will show you exactly which services touch your data before anything goes live.

It will, which is why the design assumes it. Anything consequential keeps a human approval step. Anything the model is not confident about is refused rather than guessed, and goes to the exception queue with the reason it stopped. Every decision is logged with its source, so a wrong one can be found, explained and corrected — and the monthly review reports accuracy honestly, including when it has slipped.

That is your decision, not ours, and it is worth being straight about. What we automate is the transcription and the routing — the parts of the job nobody describes fondly. Most of our work ends with the same team handling considerably more volume, and spending their time on the exceptions where judgment actually matters. We will tell you what the hours look like before you commit, so you can make that call with real numbers.

Discovery is a fixed fee agreed up front. The build is fixed-scope and quoted once we have measured the process, and the ongoing fee is monthly per live process. Model and infrastructure costs are passed through at cost, with the usage visible to you. The discovery call itself is free, and we would rather tell you a process is not worth automating than sell you a build that never pays for itself.

Thirty days' notice, no exit fee. Everything we build runs in your environment and belongs to you, and we hand over the documentation, the models and the runbooks so your team or your next partner can take it on. A business that stays because it is trapped isn't a reference.

Which process would you stop doing by hand?

Bring us the one that annoys you most. Forty minutes, no cost, and a straight answer about whether it is worth automating — including when it isn't.

Talk to a human