Agents are running and nobody can prove they work.

Your CFO built one. Your HR lead built another. The person who wrote the code is the person certifying it, and that is the sign-off legal will not take. A team cannot credibly grade its own AI work, so a Perform engineer who did not build it runs the pass. You find out what your agents do before your customers do.

Deciding what to build first

Identify the right use cases, define where value exists, and avoid spending time on AI work that never makes it into the business.

Building it into the systems you run

We wire the model into the systems, workflows and teams it is meant to serve. That connection work is the part that never gets done.

Engineers to run it

Senior engineers who take an AI initiative from plan to production.

The step between a working demo and production

Works in demoThe engineering is finished
In front of reviewThe builder is the reviewer
In productionSomeone independent signed it off
42%name risk, compliance and legal as a blocker to AI and agent work
41%name a lack of AI and agent design expertise
37%name legacy infrastructure and system incompatibility

Salesforce 2026 Connectivity Benchmark Report. 1,050 IT leaders at organizations of 1,000 or more employees across 9 countries, fielded October to November 2025. The gap between the first two answers sits within the margin of error, so governance sits level with engineering skill in that ranking.

Stuck between demo and deployed

The demo works. It never touches the system of record, the person who built it is the person certifying it, and no engineer independent of the build has checked the output. The pilot does not fail review. It never clears it.
53% of organizations have had an AI agent exceed its intended permissions. Cloud Security Alliance, 2026, 445 practitioners.
That is where it stops, and that is the part we take on.
One CTO at a mid-size enterprise: “My CFO created a mini agent, it sorta works. My HR person created another mini agent. I have twenty of those running around.”

The engineer who checks the work did not build it.

We help organizations move AI work forward where it usually breaks down most: strategy, implementation, integration, workflow fit, and delivery capacity.

Getting an AI build past review and into production

Picking one idea, fitting it to a workflow people already use, and getting it past review is where the work sits. That is the part we do.

Build

When the opportunity is clear and something needs to be designed, delivered, integrated, and launched, Perform helps move AI work from concept to production.

Consult

When the opportunity is still fuzzy, priorities are competing, or leaders need clarity before committing to delivery, Perform helps define the smartest next move.

The use case worth doing

Identify where AI can create real operational value. Plenty of ideas sound impressive and do not hold up in practice.

Copilots and internal assistants

Build the internal tools that absorb the repeat work: lookups, status checks, data entry.

Wired into your systems of record

Connect AI into the systems, data, and business processes it depends on so it supports the work your people already do.

Guardrails, evals and sign-off

Guardrails built as if the dial is already at zero, evals on every change, and a reviewer who signs off.

We work in the stack you already run

Nothing gets replaced to make room for the AI work.

Models

The model can change without the build changing, so nothing here is a dependency.

OpenAIAnthropic ClaudeGoogle GeminiMeta LlamaMistralOpen-weight, self-hosted

Cloud and data platforms

We work where your data already sits.

AWSAzureGoogle CloudDatabricksSnowflakeSQL ServerPostgreSQL

Systems of record

The work reaches production through the system of record or it does not reach production.

SAPOracleDynamics 365NetSuiteSalesforceEpicorMajor EHR platformsWMS and TMS platforms

Testing and performance

Our oldest craft, and the reason a release survives review.

Tricentis NeoLoadJMeterSeleniumPlaywrightCypressLoadRunnerSAP performance testing

Delivery and CI/CD

We ship into your existing pipeline.

GitHub ActionsAzure DevOpsGitLabJenkinsDockerKubernetesTerraform

Governance and review

The evidence a reviewer asks for, produced while the system runs.

Audit logging and evidence captureRole and access controlModel and prompt versioningEvaluation harnessesHuman sign-off gatesData residency controls

These are platforms and tools we build in. None of it is a partnership claim.

Questions about AI review and delivery

Can we get by with AI tools and open source now?

The tooling did leap forward, and plenty of teams get real work out of it. The question is who reviews what it produces and catches it being confidently wrong. That is still a senior engineer. If you have one with the time, you may not need us. If that person already carries three jobs, that gap is the work.

Can our own team sign off on the AI it built?

They can sign off, and the signature carries less weight than the review behind it. At Perform the engineer who reviews an AI build is not the engineer who wrote it. If your team built the agent, the useful review comes from outside that team.

Do you review AI that Perform built?

Yes, and never by the engineer who built it. If you want the review to come from outside Perform entirely, that is a fair ask and we will help you scope it that way.

Our security or procurement will never approve it.

That is a real gate, and finding out early beats finding out after both sides spend a month. Tell us who owns it and what they asked the last vendor for. If we cannot clear it, we will say so.

Related reading

GuideA team cannot validate its own AI GuideTen questions before a pilot ships InsightThe reviewer is the bottleneck

Start with an assessment

One use case, wired into a workflow people already use, with the review path designed in from day one. You leave the assessment knowing which one goes first and what it costs. We decline three asks: nothing started yet, a use case nobody performs, and headcount by another name.
One use case picked, with the workflow it sits in named
The review path designed in before the build starts
The cost of the first one, before you commit engineers