Most data platforms fail before the first model ships: the pipeline can't be trusted, so nothing downstream can be either. We build data and intelligence platforms pipeline-first — ingestion, enrichment, and entity resolution engineered to production standard before any decision logic goes on top.
Data is the product
Dashboards are outputs. The product is the pipeline underneath them — the thing that decides whether a number on screen is a fact or a guess. We apply a pipeline-before-policy discipline: no scoring rules, no risk models, no alerts until the data layer is provably correct. Policy written on top of an unreliable pipeline is policy you cannot defend.
That ordering has a cost. It means the first weeks of an engagement look like plumbing, not intelligence. We think that trade is worth making explicit: teams that skip it spend the next two years reconciling numbers instead of acting on them.
If your data is small, clean, and single-source, you may not need us — a warehouse and a BI tool will do. We are the right call when data is multi-source, adversarial, or regulated, and when decisions made on it must survive an audit.
What we build
- Ingestion — adapters for APIs, streams, files, and chain data. Schema-validated at the boundary. Bad records are quarantined with provenance, never silently dropped.
- Enrichment — reference data, third-party signals, and derived features joined onto the core stream, each with source and timestamp attached.
- Entity resolution — the hard part. Deduplicating and linking counterparties, accounts, addresses, and organizations across sources into a single ontology, with match confidence recorded rather than assumed.
- Decision surfaces — the layer analysts and systems actually use: queues, scores, alerts, and workbenches. Every decision surface we ship carries a trace: the inputs, rules, and model versions that produced the output, inspectable after the fact.
Everything is containerized from day one, with evaluation harnesses in CI so pipeline changes are tested against known-good outputs before they reach production.
The ORBIT discipline
ORBIT — Onchain Regulatory & Banking Intelligence Technology, a Binari incubation with its runtime in production — is our reference implementation of this discipline. Every alert ORBIT raises carries an auditable decision trace: a regulator or compliance officer can walk from the alert back through the resolution logic to the raw ingested records.
We built ORBIT the same way we build client platforms, on Aura OS, our internal AI operating system — agentic delivery pipelines, evaluation harnesses, institutional memory. It is why a small senior team ships this class of system fast, and it is applied on every engagement. The patterns transfer: transaction monitoring, fraud, credit, supply-chain intelligence — any domain where a decision must be explainable, not just correct.
Regulated by design
Regulated data changes the architecture, not just the paperwork.
- Residency — we design for GDPR-compliant data handling from the start, with EU data-sovereignty options for clients who need data to stay in-region. Dual awareness of US and EU regimes — GDPR, the EU AI Act, DORA — is built into how we scope, not bolted on before launch.
- On-prem — development and staging run on our managed on-premise infrastructure, keeping client cloud burn low during the build. CI/CD promotes to AWS, GCP, or Azure at launch — or stays on-prem in production for clients whose regulators require it.
- Auditability — decision traces are not a feature toggle; they are the default output of the architecture. If a system can't explain a decision, it doesn't ship.
Engagements start with a fixed-fee discovery sprint, run on weekly demos, and carry full IP assignment from day one. NDA on request.
Have a data problem that has to hold up under audit? Talk to us — we reply within one business day.