Services
AI Software Development for Regulated Environments
We design and build AI systems—not demos. Production-ready architecture for teams handling sensitive data under regulatory oversight.
What We Build
Most AI projects fail not because the models don't work, but because the system around them wasn't designed for production. Access controls added as an afterthought. No audit logging. Data flows that compliance teams can't trace. Models that hallucinate without guardrails.
We build AI systems designed to operate under scrutiny from day one—retrieval pipelines that keep sensitive data within your control, model guardrails that surface uncertainty instead of hiding it, and infrastructure configured for the compliance requirements of your industry.
What We Build
- Retrieval-Augmented Generation (RAG) systems — Knowledge base architecture, embedding pipelines, and retrieval design that keeps your data private and attributable
- AI-assisted workflows — Human-in-the-loop systems for clinical, legal, and compliance-sensitive processes where oversight is non-negotiable
- Model integration and orchestration — Connecting large language models to your existing data, APIs, and internal systems with appropriate guardrails
- Secure data pipelines — Ingestion, transformation, and storage patterns designed for least-privilege access and audit logging
- Hallucination mitigation — Confidence scoring, fact verification, source attribution, and human escalation workflows
- AI system monitoring — Observability for model behavior, data access patterns, and system performance in production
Our Development Approach
Architecture before code. We document data flows, access control requirements, and compliance constraints before writing a line of implementation. This avoids the expensive retrofitting that derails AI projects in regulated environments.
Built for your compliance team. Every AI system we deliver includes documentation your security and compliance teams can actually review—data flow diagrams, access control specifications, audit log schemas, and compliance control mappings.
Incremental delivery. We build in phases with validation at each step. You see working systems early, surface real issues before they compound, and maintain rollback capability throughout development.
No black boxes. We use retrieval-first approaches where possible so AI behavior is traceable. When generative models are involved, we add the logging and attribution that allows your team to understand and explain what the system did.
Frequently Asked Questions
Our primary focus is healthcare, legal, and compliance-driven organizations—industries where data sensitivity and regulatory oversight change how AI must be designed. We have deep experience with HIPAA technical safeguards, SOC 2 controls, and internal risk frameworks. If your team is building AI in a regulated environment, we understand those constraints.
Both, depending on your requirements. For many use cases, commercial APIs like AWS Bedrock, Azure OpenAI, or Anthropic provide strong security controls and BAA coverage. For organizations with stricter data residency or confidentiality requirements, we can architect solutions that deploy models within your own infrastructure. We assess your compliance obligations and recommend the approach that fits.
We design systems with retrieval-first architecture wherever feasible—AI retrieves answers from your controlled knowledge base rather than generating from opaque training data. For generative workflows, we add confidence scoring, source attribution, and human review checkpoints. Systems are designed to surface uncertainty rather than present low-confidence outputs as fact.
Most engagements begin with an architecture review to understand your data, compliance requirements, and technical constraints. From there, we move into design and build phases with regular deliverables and review cycles. We provide system documentation, compliance control mappings, and knowledge transfer so your team can maintain and evolve what we build.
What industries do you build AI systems for?
Do you use third-party AI APIs or deploy models privately?
How do you handle hallucinations and model accuracy?
What does an engagement typically include?
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Describe your current systems, goals, and regulatory requirements. We will follow up within one business day.