AI implementation for mid-market companies

AI implementation that ships inside your system of record

Your AI pilot works in the demo. Getting it into the Rails, Java or .NET app that actually runs the business is where it gets stuck. We put senior engineers in your repo to do that work, gate each go-live on numbers you agree to, and keep a rollback ready. You keep all the code.

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Building software since 2005. Austin, Texas. +1-512-200-2416

Why do AI pilots stall at the system of record?

Most stall because they were built next to the core system, not inside it. The demo called a model on clean sample data. Production means the monolith that's been running the business for ten years, with its permissions, background jobs, strange data and a team that's already busy keeping it running.

What we usually find:

The pilot team can't commit to the app that matters, or the people who own that app don't have time to review the changes.
Nobody has written down what "good enough" means as a number, so nobody can sign off.
There's no rollback plan, so all the risk lands on whoever flips the switch.
The company is paying for an AI tool, but nobody has connected it to the records it needs.

None of these are model problems. They're engineering and ownership problems, and that's the part we work on. Getting an AI pilot to production is mostly careful work in old code.

If you already have a working pilot, see our page on moving a PoC to production.

What does an Allerin AI implementation engagement include?

We pick one workflow your team already cares about and put an AI feature for it into production, inside your existing system. That includes the integration code, evals built from your real cases, monitoring, a rollback switch and a written handover.

One workflow and one number. For example: time to triage a support ticket, share of claims routed correctly the first time, or quote turnaround. We agree the baseline before we write code.
AI integration in your codebase. Calls to the model provider, retrieval over your own data, background jobs, feature flags and permission checks, written in the language your app already uses.
Evals. A test set drawn from your historical cases, run in your CI. If a prompt change or model update makes results worse, the build fails before users see it.
Monitoring. Accuracy, latency, cost per request and fallback rate, broken out by segment, not just averaged.
KPI gates and rollback. Go-live happens in stages. Each stage has a number it has to hit and a tested way back.

Still deciding what to build? Start with AI strategy. To check a workflow before go-live, use the AI production readiness assessment.

How we work: forward-deployed engineers in your repo

We're an AI development company that's been building software since 2005, so old code doesn't scare us. Our engineers work in your repository, your CI pipeline and your ticket tracker, under your branch rules. They report to your engineering lead, not to an account manager. They're senior people who've maintained large systems and know how to change one without breaking it.

Your repo, your pipeline. We don't build in a sandbox and hand over a zip file. Every change goes through your code review.
Engineering reporting line. Day to day, our engineers take direction from your CTO or VP of Engineering. A typical pilot team is two senior engineers and a part-time technical lead.
The quarter is the unit of commitment. It's long enough to ship something real and short enough to stop if it isn't working. We plan, review and renew by quarter.
A written statement of what generalises. At the end of each engagement we write down which parts you can reuse for the next workflow (the eval harness, the retrieval layer, the monitoring) and which were specific to this one. So you know roughly what the second project will cost before you start it.
A written exit plan from week one. More on that below.
Your hours. Standups, reviews and incidents happen during your working day.

How much does AI implementation cost?

Most clients start with a fixed-price diagnostic of $15,000 to $25,000, then a fixed-price production pilot of $80,000 to $150,000 for one workflow. After that you can keep engineers embedded, move to a monthly run-and-improve retainer, or take it from there yourselves.

1. System-of-record diagnostic

2 to 4 weeks. $15,000 to $25,000, fixed.

We read the code, map the data and integration points for one to three candidate workflows, and write up which one to do first, what it'll take, the KPI gate it has to pass and the main risks. You keep the write-up whether or not you go further. The full fee is credited to the pilot if you sign within 30 days.

2. Fixed-price production pilot

8 to 12 weeks. $80,000 to $150,000, fixed.

One workflow live in production, with evals, monitoring, rollback and an exit plan. Paid in four milestones:

Kickoff and agreed KPI baseline: 20%
Integration and eval harness running in staging: 30%
First production gate passed: 30%
Full rollout and handover complete: 20%

If the work takes longer than planned on the scope we agreed, we don't charge for the overrun. Scope changes are written up and priced before any work on them starts.

3. Embedded engineering

$30,000 to $40,000 per senior engineer-month. 3-month minimum, then 30 days' notice.

For when you have more than one workflow to ship, or you want our engineers working alongside yours on the next stage. Same rules: your repo, your reporting line, your code.

4. Run and improve

$10,000 to $25,000 a month.

We keep what's live working well: watch the monitoring, update evals as your data changes, handle model and provider updates, tune cost and make small improvements.

5. Outcome rider (optional)

Part of the fee tied to verified gains against the KPI baseline we agreed at the start. Measured from your systems, not ours. Terms are set per engagement.

What do you keep when we leave?

Everything. Code, prompts, eval sets, dashboards and runbooks are in your repositories and accounts from the first commit. Model provider accounts and API keys are in your name. Nothing we build needs an Allerin license to keep running.

The exit plan is a written document we start in week one and finish before we hand over. It covers:

Who on your team owns each component after we leave.
Runbooks for rollback and the incidents we think are most likely.
How to rerun the evals and what a failing result means.
How to switch model providers if you need to.
A list of every account, key and service involved, all in your name.
Paired handover sessions with your engineers in the last two weeks of the engagement.

If you call us back, it should be because the work was good, not because you can't run it without us.

Can you work with the AI tools we already pay for?

Yes. Many of the companies we talk to already pay for Microsoft Copilot, Salesforce Agentforce, ChatGPT Enterprise or Claude. Building on a contract you already have is often the fastest route to production, because security and procurement review is done.

What's usually missing is the connection to your system of record: the right records, the right permissions, and a way to test whether the answers are good. That's the work we do. If the tool you have is the wrong fit for a specific workflow, we'll say so and explain why.

Product names are trademarks of their owners. Allerin is not affiliated with these vendors unless stated.

How is this different from an AI consulting firm, staff augmentation or hiring in-house?

If you've been comparing AI consulting services, staff augmentation and hiring plans, here's the short version. We're accountable for one workflow working in production, inside your code, at a fixed price. Consulting firms mostly deliver advice or proofs of concept, staff augmentation delivers hours, and hiring delivers a team you keep. Each is the right choice in some situations.

How the Allerin model compares with other ways to get AI into production
Factor	Allerin (forward-deployed engineers)	AI consulting firm	Staff augmentation	Hiring in-house
What you get	One AI workflow live in production	Often a strategy, roadmap or proof of concept	Engineer hours	A team that builds what you direct
Who writes the production code	Our senior engineers, in your repo	Often your team, after the engagement	Contractors you direct	Your employees
Who they report to	Your engineering lead	The firm's engagement lead	You	You
Pricing	Fixed-price diagnostic and pilot, then monthly	Time and materials or a project fee	Hourly or monthly rate	Salaries, benefits and recruiting costs
Who carries overrun risk	We do, on agreed pilot scope	Depends on the contract	You	You
KPI gates and rollback	Agreed before go-live, built in	Depends on the firm	Up to you to define	Up to you to define
Who owns the code	You, from the first commit	Usually you, check the contract	You	You
How it ends	Written exit plan and handover	A final report or presentation	The contract ends and the knowledge often leaves with it	It doesn't. You keep the team
Best fit when	You have a real workflow and a system of record that's hard to change	You need to decide what to do before building anything	You know exactly what to build and need more hands	AI is core to your product for years and you can hire and keep the people

What does a KPI gate look like in practice?

A review of one deployment of Allerin's computer-vision parking product reported dashboard accuracy above 87%. A separate location was reported at 42%, with a map update still outstanding before billing could begin.

The headline figure did not establish that every location was ready. Detection performance, site configuration and operational readiness were separate questions.

That is the distinction a client pilot needs to make. We agree acceptance criteria for each site, workflow or other segment that matters before go-live. A passing overall result does not override a failing segment, and an accuracy score does not substitute for a working operational process.

Client example

AI-assisted review inside an existing Rails application

A hospitality operations software company wanted changes to its production Ruby on Rails application to reach deployment with fewer repeat problems. Reviewers kept finding the same things: unsafe database migrations, missing tests, inefficient queries and security findings. The repository already had nine automated build-and-deployment workflows, but none of them ran tests, linting or security scans. Those checks depended on each developer running local tools before submitting a change.

We connected AI-assisted code review to enforced checks in the client's own repository. A standard pull request can no longer merge without passing the required automated checks and receiving review approval. The two do different jobs. The AI reviewer reads the change, while tests, scanners and repository rules enforce specific requirements.

The first enforced run surfaced three dependency vulnerabilities and four SQL-injection warnings that were already in the codebase. Normal merges stayed blocked until each finding was fixed or given a written disposition. The controls did not quietly exempt existing problems to produce a passing result.

Industry: Hospitality operations software
System of record: A production Ruby on Rails application and the client's code repository
Workflow: Review and approval of code changes before they become eligible for deployment
Baseline: Nine build-and-deployment workflows, no server-side test, lint or security checks
Result: Required automated checks and AI review approval on every standard merge, with a documented administrative exception for urgent production hotfixes
Timeline: About three weeks from approval to the enforced gates going live, scheduled around existing releases
Left in the client's repository: Workflow configuration, branch-protection settings, an operating guide and the tracked remediation work

The change runs inside the client's delivery process. It is not a separate demonstration that their engineers still have to integrate.

Frequently asked questions

AI implementation is the engineering work of getting an AI feature into the software your business runs on, so real users and real data go through it every day. It covers the integration with your existing code, tests that measure whether the AI is right often enough, monitoring, and a way to switch it off. A demo shows something is possible. Implementation makes it part of the system.

A forward-deployed engineer is a senior engineer who works inside the client's team instead of from the vendor's side of the table. At Allerin that means working in your repository, your CI pipeline and your ticket tracker, following your code review rules, and reporting to your engineering lead. The job is to get the software working in your environment, with your data and your constraints.

No. Most AI features you'd add to a Rails app are API-driven: your code sends a request to a model, pulls in the right records, checks the answer and acts on it. Ruby handles that well, and so do Java and .NET. Python does belong in custom model training and heavier ML work. If you need that, we build it as a separate Python service your app calls, and the monolith stays as it is.

Usually 10 to 16 weeks from kickoff to a workflow in production: 2 to 4 weeks for the system-of-record diagnostic, then 8 to 12 weeks for the fixed-price pilot. Go-live is staged, so part of the workflow can be live before the pilot ends.

The diagnostic is $15,000 to $25,000, fixed, and it's credited to the pilot if you sign within 30 days. A production pilot is $80,000 to $150,000, fixed, for one workflow. Embedded engineering is $30,000 to $40,000 per senior engineer-month with a 3-month minimum. Run-and-improve retainers are $10,000 to $25,000 a month.

You do. Code, prompts, eval sets, dashboards and runbooks live in your repositories and your accounts from the first commit. Model provider accounts and API keys are in your name. Nothing we build needs an Allerin license to keep running.

If it runs over on the scope we agreed in writing, you don't pay more. That's our estimating risk, not yours. If you want to change the scope partway through, we write up the change and its price before any work on it starts.

Then it doesn't go live for that part of the workflow, and your existing process keeps running. We tell you in writing why it missed, whether it's fixable and what fixing it would take. Sometimes the honest answer is that a workflow isn't a good fit for AI yet. Better to hear that in week six than after launch.

Both. We start with Rails monoliths because that's where we see a lot of pilots get stuck, but the same approach works on Java and .NET. The diagnostic tells us early if anything about your stack changes the plan.

Yes. Using a contract you already have often makes the pilot faster, because security and procurement review is already done. We connect it to your system of record and test it like any other component. If it's the wrong tool for a specific workflow, we'll say so and explain why.

Start with a diagnostic

Two to four weeks, fixed price, and you get a written plan whether or not you hire us for the pilot. Tell us what you're trying to get into production and what it has to run inside.

Prefer email or phone? contact@allerin.com or +1-512-200-2416. Allerin, Austin, Texas. You can also use our contact page.