Build with purpose. Ship with proof.

AI-ready data, governed agents, legacy modernization accelerated by AI, and pipelines that cross the air gap.

What we do Outcomes Platforms In practice

Every program is an AI program now. Most of the data is not ready.

You are being asked for decision advantage from artificial intelligence (AI) on an aggressive timeline: onboard models from more than one vendor, keep model parity, put agentic workflows to work, and show a standardized assessment for all of it. Meanwhile the data sits in stovepipes across three networks, access is granted by policy only, and every agent you stand up needs an identity, a ceiling, and an audit trail. Speed to mission without proof is a risk nobody can authorize.

Data first. Human in the loop by construction.

Bana builds the data foundation, the models, and the delivery pipelines together, because none of them is useful alone. We are model-agnostic by design: each role routes to the model your program’s classification permits, and the harness that proves it is part of the deliverable.

Ingest, normalization, and cataloging of heterogeneous mission data (structured, sensor, imagery, logs, health and travel records) across connected and air-gapped networks: medallion landing zones, streaming pipelines, schema standardization across organizations, metadata catalogs with dynamic indexing, and policy-based access on every record.

Graph-neural, autoencoder, and statistical models for behavioral anomaly detection, with the MLOps to keep them honest: experiment tracking, a model registry, drift monitoring, and model cards. The same graph model serves identity security and pathogen-spread modeling.

A large language model agent platform for regulated environments that we designed and built. Every privileged agent action waits on a persisted, attributed, single-use human decision. The autonomy ceiling can only be lowered. Agents hold brokered, attenuated, revocable credentials with a per-use audit ledger, and compliance evidence exports against NIST SP 800-171. Agents cannot approve, waive, or publish. People do.

Single-referee evaluation harnesses, calibration reporting, published measurement ceilings, champion-challenger promotion gates with holdout regression guards, adversarial red-teaming of agent output for PII and CUI leakage, and independent cross-model code review with every finding dispositioned in an append-only log.

Decades-old systems carry science and business rules nobody can afford to lose. We modernize them with AI doing the reading and the first draft: agents analyze the code base, retire what nothing calls, propose the refactor, document it, and redesign the schema. A person approves every change, and a regression suite decides whether it ships. Speed comes from the agents; certainty comes from the gates. Proven on scientific Fortran validated to the twelfth decimal, on VB6 and Access retirement, and on a multi-year database modernization compressed into months. Where a validated computation is still right, we wrap it as a service so new systems and AI workflows call the proven science where they need it.

Private cloud enclaves in AWS GovCloud with no internet gateway, zero-trust access, permission boundaries, and landing-zone overlays, stood up in about a month and reused as a pattern, plus hybrid GovCloud-to-GCC High connectivity.

CI/CD with static and dynamic analysis, FIPS-mode container scanning, software bills of materials, hardened base images, contract tests between services, and security gates that alert on threshold breach. Pipelines carry systems from connected development across the air gap into disconnected operations, deployments move from weeks to hours, and perfective maintenance rides the same cadence.

The same models and pipelines run where there is no cloud. Our feature-extraction pipeline distilled a 133 KB edge feed from 151 MB of imagery on a stack small enough to run at the tactical edge with no cloud dependency; our identity services run at the tactical edge across four combatant commands; our GEOINT mission tooling keeps working disconnected; and our delivery pipelines carry systems across the air gap into disconnected operations. Read more under Geospatial, Identity, and Bana Labs.

35+ hours to under 1 hour

Code to production on a national-scale platform, after we rebuilt the pipeline. Infrastructure-as-code coverage rose from 35% to more than 90% in the same seven months.

Outcomes, measured.

  • 7,745 unit tests, a 44-case PII and CUI red-team corpus, and four independent inspection rounds to zero high findings on the agent platform we designed and built.
  • Test execution time cut 78 to 94 percent while coverage rose 23 to 37 percent.
  • Of more than 350 legacy scientific modules, 130 retired and 225 refactored at a 100% pass rate with zero production errors; a multi-year database modernization compressed into months.
  • Zero ATO-impacting findings across four consecutive security assessments.
  • A defense innovation biosurveillance prototype delivered through an Other Transaction with Government Purpose Rights.

Model-agnostic. Standards-native.

Python, Go, Java · React and Next.js · FastAPI and Litestar · PyTorch · MLflow · Feast · Evidently · LangGraph · LiteLLM · Amazon Bedrock · Ollama for in-boundary models · Kafka, Bytewax, Airflow, NiFi, AWS Glue · PostgreSQL and PostGIS, Redis, Redshift, Accumulo · Mesa agent-based modeling · Kubernetes, EKS, OpenShift, Helm · Terraform, Ansible, CloudFormation · GitLab CI, Jenkins · SonarQube, Fortify, Semgrep, Grype (FIPS), CycloneDX SBOM · Vault, Harbor, StackRox, Twistlock, Anchore · Istio · Prometheus and Grafana · ELK and EFK · Zscaler ZPA

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Ship on a cadence. Strengthen every release.

Every engagement runs on Start. Ship. Strengthen., and the engineering behind it is the same on every network: automation first, with infrastructure as code and containers everywhere, offline mirrors for air-gapped promotion, proof gates and screenshot gates in CI, an adversarial cross-model review before every merge, and append-only decision and audit ledgers. Continuous ATO under RMF and ICD-503 keeps the authorization moving with the code. Interfaces are open and the models are swappable, so nothing we build locks you to a vendor, including us.

How we deliver

Three missions. Same proof.

Three missions, told in full on Insights.

A defense innovation biosurveillance prototype

Under an Other Transaction, as a nontraditional defense contractor delivering with Government Purpose Rights…

Read the story

A joint cyber information-sharing environment

Our engineers ingested, normalized, and provisioned vulnerability and threat data from three security domains…

Read the story

Delivery pipeline for a national-scale federal health identity platform

We rebuilt the delivery pipeline for a platform serving more than a million users…

Read the story

Agents that answer to people.

The governed agent platform above is Bana R&D, designed and built on our own initiative for regulated environments. Agents draft in a sandbox; every privileged action waits for a single-use human decision before it is applied. It ships with 7,745 unit tests, a 44-case red-team corpus for PII and CUI leakage, and four independent inspection rounds closed at zero high findings. The same discipline produced our identity-governance and geospatial feature-extraction prototypes.

Explore Bana Labs

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Let’s talk data.

Tell us what the model needs to see and which network it lives on. We will tell you how far the data is from ready.