Capabilities, end to end
No subcontracted black boxes. The engineers who scope the work are the ones who build it, support it, and train your team on it.
Data Engineering & Analytics
Pipelines, warehousing, and mission dashboards built to hold up under real reporting deadlines.
Applied AI & Machine Learning
Model development and human-in-the-loop deployment, documented against the NIST AI Risk Management Framework.
AI Training & Enablement
Hands-on workshops that leave your engineers and analysts able to build, evaluate, and operate AI systems without us.
API Engineering & Integration
Contract-first APIs and the integration layer that lets a legacy system of record keep running while new consumers are added around it.
Security Engineering
Threat modeling at design time, least-privilege access, and scanning in the pipeline so findings surface on the pull request.
Cloud & DevSecOps
ATO-ready environments, CI/CD, and continuous monitoring for GovCloud, Azure Government, and on-prem alike.
Legacy Modernization
Mainframe and monolith decomposition, sequenced so the system the mission depends on keeps running throughout.
Technology Consulting
Independent architecture review, modernization sequencing, and build-versus-buy analysis, all written down with the tradeoffs named.
Embedded Engineering
Senior engineers working inside an existing program team, on your cadence and your tooling, with knowledge transfer built in.
Three layers that have to agree with each other
Advice that ignores the interface, an interface that ignores the threat model: that's where most modernization work quietly goes wrong.
Consulting & technical advisory
Sometimes the useful deliverable is a decision, not a deployment. We review the architecture you have, the constraints you're actually under, and the outcome you need, then come back with a sequenced plan, a build-versus-buy recommendation, and the risks stated in writing. Useful when a program has several competing technical opinions and needs a neutral read it can act on.
API engineering & integration
Contract first: the OpenAPI schema gets agreed before the implementation starts, so consumers can build in parallel and nobody discovers a breaking change in production. Authentication, throttling, and idempotency live at the gateway rather than being reimplemented in every service. Versioning is planned with a deprecation path, because in government a consumer you can't force to upgrade is the normal case.
Security engineering
Threat modeling while the design is still cheap to change, then the controls that follow from it: least-privilege IAM, secrets kept out of source, encryption in transit and at rest with key custody written down, and audit logging that can actually answer "who touched this record, and when." Static, dependency, and container scanning run in the pipeline, so a finding lands on the pull request instead of in an assessment months later.
- Security plane
- API surface
- Data platform
We build the AI system, then make sure your team can run it
A model nobody on staff can evaluate or retrain isn't a capability. It's a dependency.
Applied AI delivery
Scoped to a specific mission outcome, with the evaluation plan written before the model is chosen.
- Use-case triage, including saying when AI is the wrong tool
- Model development, benchmarking, and error analysis
- Retrieval-augmented systems over your own documents and data
- LLM application work: prompting, tool use, grounding, guardrails
- Human-in-the-loop design, escalation paths, and override logging
- Drift and quality monitoring after launch
- Responsible AI documentation aligned to the NIST AI RMF
AI training for your team
Working sessions on your systems and your data, not a generic slide deck about the future of AI.
- Hands-on workshops for engineers, analysts, and data teams
- Practical LLM use: what works, what fails, and how to tell the difference
- Building an evaluation habit: writing test sets and reading the results
- Responsible and secure AI use: data handling, review, disclosure
- Executive and program-level sessions on realistic scope and risk
- Runbooks and handover training so operations stay in-house
Where we focus
Mission areas where high transaction volume, tight scrutiny, and legacy constraints all meet at once.
Civilian Agencies
High-volume transaction systems, case management, and public-facing digital services: the platforms citizens actually touch, built to hold up under real load.
Vetting, Screening & Benefits Integrity
Identity, eligibility, and fraud-detection workflows that have to be fast, explainable, and auditable at the same time.
Defense & National Security
Modernization of mission systems in controlled environments, where security posture is a design input rather than a final gate.
State & Local Government
Smaller teams carrying the same compliance and continuity obligations, with data platforms sized to a realistic budget.
Built for the way government actually buys and runs software
Security-first architecture
NIST SP 800-53 and FedRAMP-aligned design patterns from the first sprint, so your ATO package moves faster later instead of being retrofitted at the end.
Accessible by default
Every deliverable, including this site, is built to WCAG 2.1 AA and Section 508 standards, not patched in during an audit.
A small, senior team
You get direct access to the people writing the code, not a rotating bench where the engineers who scoped the work hand it to someone else.
Transparent delivery
Progress reported weekly against the plan we agreed to, in plain language, including what slipped and why, while there's still time to react.
Most vendors either move fast and hand a program office a security debt, or move so cautiously that nothing ships on schedule. Kazaro Tech exists to close that gap.
Platforms we build on
We work on the platforms federal programs have already standardized around, rather than asking a program to adopt something new.
Amazon Web Services
GovCloud-ready architectures, change-data-capture pipelines that stream on-premise systems into managed search, and event-driven services sized for sustained transaction load.
Microsoft Azure
Azure Government workloads, event-driven functions, and managed data pipelines with automated monitoring, retry handling, and dead-letter review.
API & identity layer
API gateways fronting REST and event-driven services, with OAuth 2.0 / OIDC, mTLS between services, and rate limiting and quotas enforced in one place.
Containers & event streaming
Kubernetes and OpenShift running .NET, Java Spring Boot, and Python services, with Kafka and Redis behind them for real-time processing and sub-second reads.
Have a program in mind?
Tell us about the mission, the timeline, and the constraints. We'll tell you plainly whether we're a fit.