AI Implementation Services for the Pilot That Stalled Before Production
Staffed engineering teams, on contract or SOW, that take a working pilot through four gates into production.
- Proof
- Integration
- Exit
- Kill
AI implementation services take an AI system from a working pilot to production. KORE1 delivers them as a staffed team, on contract or under a statement of work, working the pilot through four gates. Proof, integration, exit, kill.
Last updated: October 1, 2026
- This page
- The team. Who goes onto a stalled pilot, what they build at each gate, what you keep when they leave and how the engagement gets priced.
- Covered elsewhere
- One engineer added to a team that’s already in place is AI staff augmentation. Whether to buy or build in the first place is the build vs buy question for AI, and it comes before any of this.
The pilot worked. That’s the maddening part. It answered every question in the room, the sponsor loved it, somebody said Q3 out loud, and then it met your real data, your sign-on and your compliance review, which is how two quarters later it’s still almost ready.
- 95%
- of generative AI pilots at large companies showed little to no measurable effect on profit and loss. MIT NANDA, 2025
- 28%
- of use cases fully succeed and pay back, according to 782 managers who run IT infrastructure and operations. Gartner, 2026
- 38%
- of the leaders who hit setbacks said skill gaps were still getting in the way. Gartner, 2026
The first figure is from Fortune’s report on the MIT study, and the other two are from The Register’s write-up of the Gartner survey. Read the third one again. Not the model. The people around it.
That’s the number a staffing firm can move. KORE1 doesn’t sell a model, a platform or a managed service. What we put on a stuck pilot is people, recruited by the same desk that runs our AI and machine learning engineer staffing, and pointed at a method somebody else already wrote down.
Why AI Implementation Stalls After a Good Pilot
A pilot isn’t a small version of the production system. It’s a different system that happened to produce the same demo. That line belongs to Kris Drouet, an engineering executive with 25 years in mortgage tech and fintech who publishes with us, from his breakdown of what AI implementation costs after the proof of concept. Our intake calls agree.
The pilot ran onProduction runs on
- Data
- One extract somebody tidied by hand
- The live feed, from systems that don’t agree
- Traffic
- A capped test budget
- Every user and every retry, all month
- Review
- Security approved a sandbox
- Logs and documentation an auditor will accept
- People
- An engineer on loan from another team
- Somebody whose job it is
Look at the right-hand column. A pipeline, retry logic, an audit trail, an owner. None of it was in the demo, and all of it is work that has to be assigned to a person, which is why a pilot with no team behind it sits at nearly done until the budget wanders off.
The base rate isn’t kind either. A 2024 RAND report notes that by some estimates more than 80% of AI projects fail, twice the rate of IT projects with no AI in them. Kris keeps his own count. Ten pilots go in and two come out running.
Four Gates Between the Pilot and Production
The gates are Kris’s. He wrote his four-gate checklist for the week before you sign a vendor. We run it late, on a pilot that already exists, because in his words a late gate still beats no gate.
As we usually find it
- ProofA demo
- IntegrationA sandbox key
- ExitNever tested
- KillNo number
The day we leave
- ProofEval report
- IntegrationLoad test
- ExitExport log
- KillKill memo
Gate one
Proof
- The question
- What has this been proven on, besides the demo?
- As found
- A demo on a sample one person cleaned up. A rehearsal.
- The team builds
- A labeled test set pulled from real traffic, and a scored eval suite that reruns on every change. Kris makes the longer case in evals before features.
- Seats
- An ML or LLM engineer, plus a reviewer from your side who knows what a right answer looks like.
- Witness mark
- An eval report. One score on your data, with the test set sitting in your repo.
Gate two
Integration
- The question
- What does wiring it into the core systems really take?
- As found
- A sandbox, and an API key somebody emailed around.
- The team builds
- Real sign-on, data residency, queues, retries, writes that are safe to repeat, and pipelines for the live feed in place of the clean extract.
- Seats
- A data engineer and a backend engineer. This gate eats the most hours. Plan for that.
- Witness mark
- A load test at production volume, and a runbook for the day the vendor changes its API.
Gate three
Exit
- The question
- How do we leave, and what does leaving cost?
- As found
- Prompts, data and outputs sitting wherever the vendor keeps them. Nobody has run the export.
- The team builds
- An export that has been restored somewhere else for real, and a thin layer between your code and the model, so swapping one out is a config change.
- Seats
- An MLOps or platform engineer.
- Witness mark
- An export log and one rehearsed model swap. Done on purpose, on a quiet day.
Gate four
Kill
- The question
- What number, by what date, ends this?
- As found
- A dashboard. No threshold and no date.
- The team builds
- Cost tracing per feature, the one business metric tied to a figure finance already tracks, and a threshold in writing.
- Seats
- The technical lead, often with an AI product manager.
- Witness mark
- A one-page kill memo. The number, the date, the owner’s name.
Gates, not a score. One hard no stops the work, however well the other three went, and a team that averages its way past a failed export test hasn’t hardened anything, it has only moved the failure to a month when it costs more.
Who Sits on an AI Implementation Team
Small on purpose. A technical lead, two or three builders, and whichever specialist the stuck gate calls for. Every seat is a US-based engineer on a KORE1 W-2, which keeps payroll, benefits and the employment paperwork on our side of the table, and you interview each one before they touch your repo.
- Technical leadCarries every gate and can call a no. A senior engineer who has shipped a model before. Not a project manager.
- ML or LLM engineerBuilds the eval suite, then fixes what it finds. We recruit both machine learning engineers and LLM engineers, and which one you need depends on what the pilot is made of.
- Data engineerReplaces the hand-cleaned extract with a pipeline. Often the first data engineering seat we fill.
- MLOps engineerServing, monitoring, rollback and the exit test. The MLOps hire gets skipped more than any other, and then every deploy is a favor somebody owes.
- AI product managerPart time on most teams. An AI product manager holds the one number.
Not every pilot needs five. A retrieval assistant stuck on evals might need two people. An agentic workflow that writes back into your ERP could need the whole table, plus somebody who knows the ERP, and we’d sooner tell you that on the first call than discover it in month two.
Contract Team or SOW Team
Same engineers either way. What changes is who points the work each morning and what makes an invoice go out.
Your lead
A contract team by the hour
Someone on your side runs the gates, our engineers take direction from them, and the invoice follows the timesheet.
Contract staffingOur lead
A SOW with four milestones
One payment per gate, released when you accept that gate’s witness mark instead of on a calendar date.
SOW staffing servicesOne gate
A single specialist
When the team exists and one gate is stuck, add the missing engineer to the people you have.
Generative AI engineer staffingAfterward
The operator who stays
A production system needs a named owner, and we can recruit that person before the team rolls off.
Direct hire staffing
On rates. Our AI engineer contract rates guide puts a mid-level US contractor at $125 to $180 an hour through an agency, and a senior at $170 to $240, which is the bill rate you pay and not what the engineer takes home. Budget the run as well as the build. Kris plans for production to cost three to five times the pilot, and by his account five is the likelier end.
92%
KORE1 retention at the one-year mark
17 days
our running average to fill an IT seat
2005
KORE1 opened that year in Irvine, California
30+
U.S. metros where our recruiters place engineers
Figures from Why KORE1.
When the Gates Say Stop
Sometimes the right result of an AI implementation engagement is a dead pilot. The eval comes back at a number nobody can defend, or the export turns out to be impossible, or the metric doesn’t move by the date everyone agreed to back when the pilot still looked like a sure thing. It happens. Then the team says so, in writing.
- Under a SOW
- Stopping at a gate ends the engagement at that milestone. Nothing past it gets billed.
- On a contract team
- You release the seats, and the hours stop with them.
- Either way
- A no that shows up early is the cheapest one you’ll get. You keep what was built, and by then that usually includes the test set and the eval suite. Passing it to whoever runs it next is a job of its own, covered in a knowledge transfer plan that keeps you from getting locked in.
Our bias, stated plainly. KORE1 earns money when teams get staffed, so a pilot that goes forward is good for us. That’s why the kill memo is written by the technical lead and signed by your sponsor. Not by us.
Common Questions
What do AI implementation services include?
A team, a method and four documents you keep. The team is a small group of engineers on contract or under a SOW. The method is four gates. The documents are an eval report, a load test, an export log and a kill memo, and each one closes a gate.
How is this different from hiring an AI consulting firm?
Ownership, mostly. A consulting firm arrives with its own platform, playbook and people, and you receive a deliverable at the end. Here the engineers work in your repo and report to your lead or to ours, and everything they build stays with you when the contract ends.
How long does it take to get a stalled AI pilot into production?
Seventeen days or so to staff the first seats, which is our running average for filling an IT role. After that it depends on which gate the pilot is stuck at, and we won’t quote weeks until the lead has read the code. Integration runs longest. Almost always.
What does an AI implementation team cost?
$125 to $180 an hour for a mid-level US AI contractor through an agency, and $170 to $240 for a senior, per our 2026 rates guide. Multiply by seats and weeks. Then set aside the run cost, which tends to run three to five times the pilot.
Can you take over a pilot that another vendor built?
Yes, if you can get at the code, the data and the prompts. Checking that access is the first thing the exit gate does. When a vendor won’t release them you’ve found the real problem early, and it’s a contract conversation before it’s an engineering one. Usually solvable.
What if the pilot shouldn’t go to production at all?
Then the kill gate did its job. You get a one-page memo with the number it missed and the date, you stop paying for seats, and the budget moves to the next idea. Cheap, next to funding it for another year.
Who runs the system after your team leaves?
Someone with a name. A production AI system needs an owner for evals, drift and the alert that fires at night, and we can recruit that person as a direct hire while the team is still working. Our twelve-month retention sits at 92%, and here that counts for more than speed.
Got a Pilot That Won’t Ship?
Tell us what it does and where it stopped. A paragraph is plenty. A recruiter who staffs AI teams calls back with the gate we’d start at, the seats it needs and a rate range.
Brief Us on Your Pilot →Or call 949-706-6990
