AI implementation services that you pay for after it works
Our AI implementation services take one workflow from your team and put it into production in your own environment. We agree the acceptance criteria with you on day one and build for two weeks. You pay $10,000 only after every criterion is met.
Most AI projects stall between the pilot and production.
Discovery that never ends
Weeks of workshops produce a roadmap, and the first working system is still a separate statement of work away.
Success was never written down
Nobody agreed what good looks like before the build, so the demo gets judged on how it feels in the room.
Built somewhere else
The pilot ran in a vendor sandbox on sample data. Moving it into your tenant turns into a second project.
You pay for effort
Time and materials billing means the invoice arrives whether the system works or not.
Our AI implementation process, step by step.
Every engagement follows the same five steps. We run one workflow at a time, and each one ends with a system your team uses on real work.
- 01
Scope one workflow
Before day oneYou tell us the workflow and who does it today. We look at real samples of the work and mark where a person needs to approve the output. Within 48 hours you get back a fixed price and draft acceptance criteria.
- 02
Sign the acceptance criteria
Day oneWe sign an agreement that lists the deliverables and the tests the system must pass, written in your terms. A typical criterion reads like this: given last month's invoices, the agent matches each line to the right purchase order, and a reviewer approves or corrects every match.
- 03
Build in your environment
Two weeksWe build inside your tenant or cloud account from the first commit, against your own data and access rules. You see working software during the build, with no slide reviews in between.
- 04
Run the acceptance tests
End of week twoWe run every signed criterion with your team watching, on your data. If a criterion fails, we fix it and run it again. You pay $10,000 only when every test passes.
- 05
Hand over
On acceptanceYou get the code in your repository, the prompts and test sets, a runbook and a walkthrough for the people who will own it. Then you decide whether to start the next workflow.
Enterprise AI implementation services across agents, search and Copilot.
Most of our work falls into four kinds of system. The first is agentic AI, where an agent reads documents, checks them against your rules and drafts the next action for a person to approve. The second is retrieval over your own content, often called RAG, where answers come with citations and respect the permissions each person already has.
The third is Microsoft 365 Copilot and Copilot Studio, where we build agents inside Teams and SharePoint that your staff already open every day. The fourth is custom AI in your own cloud account on Azure or AWS, and on Google Cloud for teams already there. We use services such as Azure OpenAI and Amazon Bedrock, with models from providers such as OpenAI and Anthropic.
A European pharmaceutical regulator runs an AI compliance scanner we built: 620+ marketing assets scanned, 11 rules per scan, about 2 minutes per asset, down from 2 to 3 hours of manual review. Most first builds are smaller than that, usually one document-heavy task for one team.
An AI implementation company that bills on acceptance.
Plenty of good firms do AI implementation consulting. The difference with us sits in how the engagement is shaped and when money changes hands.
| Question | Typical consultancy engagement | QueryNow |
|---|---|---|
| When you pay | Monthly or by milestone, often with a deposit upfront | Once, after every acceptance criterion passes |
| How price is set | Time and materials, or an estimate that can grow | $10,000 fixed per workflow |
| When success is defined | Often during or after discovery | In a signed agreement on day one |
| Where the system runs | Frequently a vendor sandbox first, then a migration | Your tenant or cloud account from the first commit |
| First deliverable | A strategy deck or roadmap is common | A working system on your data |
| Who owns the result | Varies by contract | You own the code and the test sets |
Check your readiness, then pick a partner.
If you are comparing AI implementation partners, these pages help you arrive with a sharper brief.
Superintelligence Readiness Check
Eight questions on data access and approvals. About three minutes, no email needed.
Open →Acceptance criteria generator
Draft the tests your first workflow should pass before you talk to any vendor.
Open →How we work
The full method behind the two-week build, including what we need from your team.
Open →Pay when it works
How the pricing works, what counts as acceptance and what happens if a test fails.
Open →What every AI implementation includes.
A signed definition of done
Deliverables and acceptance tests agreed in writing before the build starts, so nobody argues about success later.
Human approval built in
A named person reviews the outputs that matter, and every approval is logged with who made it and when.
Agent identity and least privilege
Each agent runs under its own identity with the least access it needs, inside your tenant.
Permission-aware data access
Retrieval follows the access your people already have, so an agent never shows a document someone could not open.
Model portability
Prompts and test sets live in your code, so you can compare a newer model against the same tests and switch when it wins.
A baseline and a measure
We record how long the work takes before the build and measure it after, so hours saved are counted.
From workflow to working tool in two weeks.
Scope and sign
We define the workflow, the deliverables, and the acceptance criteria, and sign an agreement on them before anything starts.
Build
We build the tool in your environment, with Claude Code and automated evaluation against your own data.
Pay when it works
$10,000, due only after every criterion in the signed agreement is met. Nothing before that.
We scope one workflow with you and sign the acceptance criteria on day one. We build it in your environment in two weeks, and you pay $10,000 only after every criterion is met. Nothing upfront. Larger programs run as a series of these builds, priced together once we know the portfolio.
See what AI implementation costs →
Tell us the workflow →Questions buyers ask.
What are AI implementation services?
AI implementation services take an AI use case from idea to a system people use in daily work. That covers scoping, data access, the build itself, testing against agreed criteria and handover to the team that will own it. Our version runs one workflow at a time in two-week builds.
What does the AI implementation process look like?
Five steps. We scope one workflow, sign the acceptance criteria on day one, build in your environment for two weeks, run the acceptance tests with your team and hand over the code and runbook. You pay only after the tests pass.
How much do AI implementation services cost?
Each workflow is $10,000, fixed, paid only after every acceptance criterion in the signed agreement is met. Nothing is due upfront. Programs that cover many workflows are priced together once the portfolio is clear.
What happens if the system fails an acceptance test?
We fix it and run the test again. The invoice goes out only when every criterion passes. If a workflow turns out to need data or access you cannot provide, we tell you early and agree a change in writing before going further.
Do you offer agentic AI implementation services?
Yes. Many of our builds are agents that read documents, check them against your rules and prepare the next action, such as drafting a reply or updating a record. A person approves anything that matters, and each agent runs under its own identity with limited access.
Which platforms do you implement on?
Microsoft Azure, Microsoft 365, AWS and Google Cloud. We use the platform you already run, with services such as Azure OpenAI, Copilot Studio, Amazon Bedrock and Vertex AI, and we keep prompts and rules in your code so you can change models later.
How is an AI implementation partner different from AI implementation consulting?
Consulting often ends with a recommendation. An implementation partner is accountable for a working system. We do both in one engagement, because the scoping step produces the criteria we then build against and get paid on.
Who owns the code and data?
You do. The system runs in your tenant or cloud account, with the code in your repository. Your data stays where it is today. We hand over the prompts and test sets too, so your team or another vendor can keep improving it.
Ready to ship AI that actually works?
Send us the workflow. We return a fixed scope, price, and acceptance criteria in 48 hours. You pay $10,000 only after it works.
Tell us the workflow →The mid-market AI advantage: How $50M to $500M companies out-ship the Fortune 500
Why $50M to $500M companies get AI into production faster than the Fortune 500.
Read it free →