AI DISCOVERY
The engineering assessment that maps your AI build and proves it holds.
Algorama engineers work inside your environment, assess where AI delivers measurable return, and produce a scientific-grade blueprint alongside a working demo run against your real data.
ENTERPRISE-GRADE BY DEFAULT
WHO IT'S FOR
AI Discovery is where every engagement starts. Most organizations are under pressure to move on AI with no clear picture of where to start. Discovery maps where AI creates leverage in your operations and documents what to build, in what order. You leave with a working demo run against your real data. You know exactly what the build entails.
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HOW IT WORKS
The Algorama process.
02 Build
Development runs in your systems. We configure, build, and integrate. Your team owns the system.
03 Support
Ongoing support after deployment. We keep the system running as your environment evolves.
AI Discovery is step one.
WHAT'S INCLUDED
What you get out of your AI Discovery engagement.
HOW WE DELIVER
We start with what you already have running: the pipelines, the tooling, and the data. Failure modes are documented as part of the assessment.
01
Scope
We map your architecture, access requirements, and compliance constraints. The result is a clear picture of your environment and what the engagement covers.
02
Advisory
We run working sessions on your operations, workflows, and data. Use cases are ranked by impact and engineering effort, sequenced around your actual constraints.
03
Demo
We build and run a working AI demo in your systems against real data, including edge cases and bad inputs. The approach is validated before production.
04
Brief
We produce the final brief: build sequence, architecture options, and failure modes documented for the engineers who will own the build.
PROOF
CASE STUDY · PROPTECH SAAS
A 30% failure rate, diagnosed and eliminated.
Flow Intelligence's AI was failing on 30% of live production traffic. A hardcoded single-model integration with no persistent memory and no access controls was the architecture decision. Discovery named it as the failure point before the rebuild was scoped. The resulting system was built as a fully owned architecture with a semantic query layer, granular RBAC, and custom data connectors.
✓
Failure rate: 30% → 0. Production uptime: 99.9%.
✓
They own the full codebase, agents, and data layer.
Read case study
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FAQ
What does "full IP ownership" mean in practice?
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How is this different from buying an AI platform?
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Can Algorama work alongside third-party AI platforms we already use?
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How long does a build take?
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Where does our data live during training?
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What happens after the build is delivered?
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Bring the problem. We'll map the build.
A 30-minute call with the engineers who would run your engagement. You leave with a clear picture of where to start and what we can produce for your environment.
