Services
AI implementation services
AI implementation services take an AI system from a first pilot to daily operation inside a real business process. The hard part is the move from a controlled share of live cases to full volume with every control in place. Manthle's AI engineers take one critical process at a time through that move, then run it with your team.
You get one process that used to depend on people copying, checking and chasing, now running on an AI system that does the routine work and hands the judgment calls to the people best placed to make them. After go-live, Manthle's engineers run it with your team.
That is the whole offer. Manthle does not start with an enterprise AI program. It starts with one process that is too important to stay manual, too specific for off-the-shelf software and too complex for basic automation, and makes it work in production.
What you get
- A working system in your process. It reads cases from the systems you already run, acts within written limits, and writes results back where your people expect them.
- An exception path your team trusts. Cases the system should not decide reach a named person with the context gathered, and that person's decision is recorded.
- A baseline and a scoreboard. Cycle time, cost per case, backlog and error rate measured before the build, then tracked against that baseline as the system runs.
- A pilot sized to become the contract. When the pilot on one process works, it turns into a monthly operations contract. Nobody has to restart the project to make it permanent.
How the engagement runs
| Step | What happens in your operation | What you have at the end |
|---|---|---|
| See | Engineers work through the process with the people doing it, pull a sample of real cases and trace each one through your systems | A map of how the process runs today, including the workarounds nobody wrote down |
| Rethink | Your process owner and the engineers decide which cases the AI closes, which it prepares for a person and which steps disappear | An agreed target process, with a named handler for each kind of exception |
| Engineer | The AI system is built into your existing tools, with the exception queue designed alongside it | A pilot running on a controlled share of live volume, with its limits, approvals, logging and fallback already on |
| Harden | The engineers and your risk and IT teams complete what the system may never do, how an outage is handled and what gets logged, then take it from the pilot's share to full volume | Approval from risk and IT before full volume |
| Run | The system operates at full volume, with engineers watching the numbers and your team on the exceptions | A monthly operations contract and a process that keeps improving |
The first two steps matter more than most buyers expect. A buyer who skips them pays to automate steps that exist only because two systems never talked to each other. The AI implementation guide walks through each step in detail, and the roadmap guide covers sequencing and decision gates.
What Manthle does, and what it leaves to others
Manthle's AI engineers take one process at a time and stay with it. They study the work, redesign it with your process owner, build the AI system, harden it and run it.
The engagement's scope is one process, built and then run. A multi-year AI strategy, an enterprise-wide technology selection and a build handed over on the last day of a project fall outside it. The working model is closer to forward-deployed engineering: engineers who work inside the process with the people who run it, not behind a ticket queue.
Where your people stay in charge
Your people keep the decisions that need judgment: the disputed charge, the unusual claim, the customer who needs a call. They keep authority over the rules, so a policy change is theirs to make and the system follows it. The design includes a way for your team to pause the system or switch it to a fallback, and the process owner decides when to use it. Ask any partner, Manthle included, to show where each of those controls sits in the system before the pilot starts.
How to evaluate an AI implementation partner
Use this checklist in the first two meetings. A partner who cannot answer most of it is selling something other than implementation.
- Did they ask to see the process and real cases before proposing a model or a price?
- Will the pilot run on your data, inside your systems, or in a separate sandbox?
- Who owns the exceptions on day one, and how do those cases reach a person?
- Are the success measures operational (cycle time, cost per case, backlog, error rate) and fixed before the build?
- Who runs the system the week after go-live, and the month after?
- How is each automated decision logged, and can an auditor trace it?
- What happens when an upstream system is down or sends malformed data?
- Does the design include a way for your team to pause the system or switch it to a fallback on its own?
One survey points toward outside partners, with caveats. In the self-reported sample behind MIT NANDA's "The GenAI Divide" (July 2025), "external partnerships with learning-capable, customized tools reached deployment ~67% of the time, compared to ~33% for internally built tools." The authors note that these figures "reflect self-reported outcomes and may not account for all confounding variables," and that the correlation "does not necessarily prove causation," a fair warning whenever a vendor quotes it. The same report describes the most successful buyers as ones who "act like BPO clients, not SaaS customers" and "hold vendors accountable to business metrics," which is what the checklist above is for.
When a different kind of firm fits better
- You need an enterprise AI strategy. If the question is where AI should go across twenty business units, a strategy consultancy is built for that work.
- The process is stable, structured and rule-based. Plain automation or RPA will be cheaper. The comparison of AI and automation sets out where each fits.
- You do not yet know where the process breaks. Process mining can show the real flow from system logs before anyone builds; see process mining vs RPA.
- A packaged product already handles the process well. Buy it and configure it.
- The job is a core-system migration. Moving to a new ERP or billing platform is a systems integrator's work. AI on top of a system mid-migration is usually wasted effort.
You know what happens before we start.
One process, five steps. A pilot is measured against goals agreed at the start, and once it meets them, it becomes a monthly operations contract.
- 01
See
We spend time inside the process: its history, its systems, and the people who settle the hard cases by habit.
- 02
Rethink
With your process owner, we redesign before anything is automated: which decisions belong to a model, which to a rule, and which to a person.
- 03
Engineer
We build the AI system around the redesigned process, connected to the systems you already run, with the exception queue designed alongside it.
- 04
Harden
We test it on the cases that break things: incomplete inputs, odd edge cases, systems that go down.
- 05
Run
We operate it with your team, and keep it dependable as models, systems and policies change.
Questions
A full engagement covers studying the current process, redesigning it, building the AI system inside your existing tools, hardening it for production and operating it after go-live.
A consultancy usually advises on where AI should go. An implementation partner builds the system, puts it into production and stays accountable for how it performs.
Pick a manual process that matters to customers or cash, runs at real volume, has a clear owner and is too specific for an off-the-shelf product to handle well.
With Manthle, a pilot that works on one process becomes a monthly operations contract, and Manthle's engineers run the system with your team.
People stay in the design: the AI system handles routine cases, and the people who know the process handle exceptions, approve sensitive actions and decide when rules change.