Enterprise AI solutions
Enterprise AI that can show its work.
Solfara builds AI systems that run inside your systems of record — turning evidence you already own into answers your architects, your auditors and your executives can all defend.
Built for regulated environments — healthcare, financial services and critical infrastructure.
Control conformance
Basis · confidence
- Meets
Disaster recovery · RTO commitment
Q4 DR test report · vendor record
Evidenced - Gap
Regional separation of data tier
CMDB host inventory · single region
Corroborated - Violation
Software currency · supported release
Currency scan · OS past end-of-support
Evidenced - Needs info
Backup immutability
No evidence located
None
Illustrative output. Verdicts are produced by a model reading labelled evidence; the risk arithmetic is computed in code.
The difference
The distance between a demo and a decision
Enterprises rarely struggle to generate AI output. They struggle to trust it enough to act on it. That gap is a design problem, and it is the one we build against.
Demos summarise. Decisions need provenance.
A fluent paragraph is not an answer anyone can sign their name to. Every claim our systems make arrives attached to the evidence it rests on — and says plainly when there is none.
Your data is not in one place, and never will be.
The signal lives across a CMDB, a resilience platform, a data warehouse and a vendor PDF nobody has opened in two years. We integrate the systems you actually have, including the ones without a usable API.
Judgement belongs to the model. Arithmetic does not.
Language models read and weigh evidence. Scores, thresholds and rollups stay in deterministic code — so the same inputs always produce the same number, and anyone can audit how it was reached.
Solutions
Four outcomes we build toward
Each one begins with the same question: what evidence do you already hold, and what is it worth once it is assembled?
Our approach
Confidence is an output, not a feeling
Every verdict our systems produce carries its basis: what it rests on, and how much that is worth. An answer with no evidence behind it is reported as exactly that — not quietly rounded up into reassurance.
In one healthcare portfolio, introducing vendor disaster-recovery test reports as a fourth evidence layer moved a set of critical controls off attestation-only — and dropped the application's measured risk by roughly a fifth. Nothing about the environment changed. Only what we could prove about it.
The confidence ladder
None
No evidence either way. An open question, counted as risk.
Attested
Someone declared it. Nothing corroborates the declaration.
Corroborated
A declaration that independent observed data agrees with.
Evidenced
A document, test result or measurement proves it. Cite it.
The work is moving verdicts up the ladder — and flagging what refuses to climb.
How we engage
Sequenced so value arrives before the platform does
We do not ask for an eighteen-month platform programme on faith. Each phase produces something usable on its own.
Assess
Four to six weeks mapping where evidence already exists, where decisions are being made without it, and what is genuinely worth automating.
02Build the substrate
Reach the systems of record, reconcile them to a common key, and make the result queryable. Most AI programmes fail here, before a model is ever involved.
03Put AI where it earns its place
Grounded agents, retrieval and evaluation harnesses — shipped into the workflow, measured against real outcomes, and instrumented for cost.
Work
Systems already running in production
Environments are described without naming them. The engineering is described exactly as it happened.
Start with your hardest question.
Tell us the decision your organisation keeps making without good evidence. That is usually the right place to begin.