Impact

The model is the easy part. Operationalizing AI around real business problems is what creates value inside the enterprise.

Custom systems of intelligence, running in production inside enterprise processes that have to be predictable and repeatable. Our experienced engineering crew, amplified by Agent SIs, helps turn real business problems into measurable outcomes without adding unnecessary complexity. Each build makes the next one faster.

Selected production systems

The answers were already in their systems. Nothing could act on them in time.

Built and running inside sixteen weeks. Each one faster than the last because it reuses what was already built.

/ 01

Reclaiming capacity

/ 02

Better discovery

/ 03

Faster access and approvals

/ 04

Governed data movement

01 · Reclaiming capacity

Freeing capacity for higher-value work

Capacity that used to sit idle until a monthly review now moves to the work that needs it inside the billing cycle. Every team sees what it is using, and what it will use by month end. The recovered cost is real. The capacity that went back to work is worth more.

The problem

Compute spend was spread across subscriptions, environments and teams, with no single view of what was idle, oversized or likely to cost by month end. Existing tooling could report what had already happened, but it could not identify the savings opportunity, recommend what to do next or act on it.

What we built

A multi-agent system reads the platform’s usage and cost data, identifies idle and oversized capacity, calculates the financial impact and recommends what to do next. A human approves the action, the system executes it and records what happened. Spend is also attributed and forecast so teams can see where cost is going before the billing cycle closes.

What changed

Teams can see where compute spend is going and where savings exist before the billing cycle closes.

Idle and oversized capacity becomes an actionable recommendation rather than something found in a monthly report.

The recovered cost is real. The capacity that goes back to work is worth more.

01 · Reclaiming capacity

Freeing capacity for higher-value work

Capacity that used to sit idle until a monthly review now moves to the work that needs it inside the billing cycle. Every team sees what it is using, and what it will use by month end. The recovered cost is real. The capacity that went back to work is worth more.

The problem

Compute spend was spread across subscriptions, environments and teams, with no single view of what was idle, oversized or likely to cost by month end. Existing tooling could report what had already happened, but it could not identify the savings opportunity, recommend what to do next or act on it.

What we built

A multi-agent system reads the platform’s usage and cost data, identifies idle and oversized capacity, calculates the financial impact and recommends what to do next. A human approves the action, the system executes it and records what happened. Spend is also attributed and forecast so teams can see where cost is going before the billing cycle closes.

What changed

Teams can see where compute spend is going and where savings exist before the billing cycle closes.

Idle and oversized capacity becomes an actionable recommendation rather than something found in a monthly report.

The recovered cost is real. The capacity that goes back to work is worth more.

02 · Better discovery

Turn a question into an actionable answer in minutes

Finding the right module, tool or dataset meant relying on what you already knew, asking a colleague, or manually reading through descriptions that were not always complete, consistent or easy to interpret. Now you describe what you need in plain language, and the system surfaces the most relevant options, including ones keyword search would have missed.

The problem

Researchers often knew the problem they were trying to solve, but not the exact module, tool or dataset that could help. With hundreds of resources to choose from, keyword search only worked if you already knew what something was called. People relied on what they already knew or asked colleagues, while useful tools and datasets remained hard to discover.

What we built

Researchers describe what they need in plain language. The reasoning engine interprets the intent, searches across modules and datasets, and ranks the results by fit. Each result explains why it matched, so the researcher can judge the recommendation rather than simply trust a search result.

What changed

Researchers search by what they are trying to accomplish, not by the exact name of a module, tool or dataset.

Relevant resources based on semantic understanding surface faster, including ones keyword search would have missed.

Every result shows why it matched, so people can judge the answer for themselves.

03 · Faster access and approvals

Cutting access decisions from weeks to days

Time to access sensitive data resources fell from weeks to days, and the decision itself now takes minutes. Every decision is recorded with the reasoning behind it, so the process can be improved rather than reconstructed later.

The problem

Getting access to a data source meant manual triage, email chains and chasing reviewers, with no shared view of where a request stood. Approvals could take weeks, even when the eventual decision itself was straightforward.

What we built

A multi-agent workflow carries each access request from submission through decision. Requesters and reviewers work from the same live status. Reviewers get an analysis, recommendation and the reasoning behind it, while the human remains responsible for the decision. Policies are applied at the point of action, and every step is recorded for audit.

What changed

Access that took weeks can now move in days, with the decision itself taking minutes.

Manual chasing and status checks are replaced by one live view of every request and its next step.

Reviewers get the analysis and reasoning needed to make a faster decision, while the human remains in control.

Every decision is traceable and auditable from request through outcome.

04 · Governed data movement

Risk scored before anything leaves

We removed the manual guesswork from sensitive-data review. Reviewers can clear low-risk files in bulk, with the checks that cleared them visible. Higher-risk files are surfaced with the evidence behind the risk, and every decision is captured with a clear, auditable rationale.

The problem

Reviewing files for sensitive data was a manual, slow and inconsistent process. Reviewers had to determine whether content could safely move into broader environments without exposing protected information. That judgment was hard to make quickly and consistently, so teams defaulted to caution and work slowed down. And risky files still got through, only turning up in an audit later.

What we built

Every file is evaluated before it leaves the platform. Low-risk files can clear in bulk, with the checks that cleared them made visible. Higher-risk files are flagged with the specific reason and routed to a human when judgment is required. Each request is evaluated in real time, and every agent can access only what it is explicitly permitted to reach. The decision, the evidence behind it and the reviewer’s action are all recorded, so governance happens as part of the workflow rather than after the fact.

What changed

Legitimate work moves faster because reviewers spend their time on the files that actually carry risk.

Review decisions are faster and more consistent because every flagged file comes with the evidence behind it.

Reliance on manual inspection is reduced, lowering the risk of human error and inconsistent handling of sensitive data.

Governance is auditable by default, with every decision and rationale captured as the work happens.

Less manual work. Faster execution. More capacity for higher-value work.

How it gets built

AI makes generating software faster. The harder part is defining exactly what the system should do, what it may touch, what must never happen, and proving that it behaves as intended. Agent SI Workbench makes that specification the contract for the build. Your team approves what gets built and validates that it behaves as intended.

01

Specify

Discovery material goes in: playbooks, policies, API specs and how the work actually runs today. FDE Helper Agents turn that into a specification that defines the agents required, what each may access, what must never happen, the expected behavior, and how the system will be validated.

↓ A human signs off

Your team reviews the specification, its assumptions and known gaps. Nothing gets built until you approve what the system is expected to do.

02

Generate

Builder Agents work from the approved specification, reusing what already exists and building new only where needed. Every output is tested against real recorded responses and the requirements your team approved. What matters is not how much code was generated, but whether the system behaves as specified.

↓ A human signs off again

Your team reviews what was built against what was approved. That second approval is what releases it to run.

Independent by design

We are not tied to a model, cloud or enterprise software platform. We work across your stack and in your environment, choosing what best serves your requirements and business outcomes.

03

In production

The workflow runs in your environment, on your infrastructure, with every execution traced. As it runs, it captures decisions, outcomes and operating context, so the system can surface better insights and improve how similar work is handled over time. The agents, connectors and proven workflow patterns become reusable building blocks, so the next workflow starts from what this one already learned and proved.

How it compounds

Everything built and learned makes the next one faster

Every workflow runs in your own cloud, on your infrastructure, on the model of your choice. You own everything we build. Underneath them is one reasoning engine, one governance model, and one growing body of context that each workflow strengthens for the next.

Get started today.

Get in touch

Get started today.

Get in touch

Get started today.

Get in touch