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San Diego, CA: Regulated-Industry SaaS Engineering
Adding AI to a SaaS product that serves healthcare, defense, or biotech clients is a different problem than adding AI to a generic B2B tool. Your customers have compliance teams, audit requirements, and sometimes federal regulators looking over their shoulder. The AI features you ship have to survive that scrutiny.
We build AI features with audit trails from day one, explainability outputs that your customers can show their compliance teams, and toggle controls so enterprise buyers can disable AI for their account when their procurement process requires it. We do not paper over these concerns after the fact; we design for them at the start.
Describe your product and the compliance constraints
We will tell you what is feasible, what the audit trail architecture looks like, and what it costs. No pressure, no sales process.
Generic AI integration advice does not account for regulated-industry requirements. Here is what is different when your customers have compliance teams.
Every AI-generated output gets a permanent, structured log entry with model version, prompt version, input hash, timestamp, and user context. Append-only. Queryable. Your customers can request a full audit trace of any AI action.
AI features that output a classification or recommendation also output a plain-language rationale. Not just "category: urgent" but "classified as urgent because the claim contains terms matching these three criteria." Your customers can show this to their own auditors.
We document where your AI features sit on the FDA's Software Functions Affecting Safety taxonomy, design human-review gates for higher-risk outputs, and help you maintain a clear intended use statement that does not inadvertently create a device claim.
Every AI feature ships with tenant-level and user-level disable controls. Regulated enterprise buyers routinely require the ability to turn off AI features for their account. This is in the spec before a line of code is written.
San Diego has an unusually high concentration of SaaS products that touch regulated industries. ServiceNow customers in the region include military and healthcare organizations with strict IT compliance workflows. Qualcomm's developer toolchain handles code from defense contractors. Biotech informatics platforms at companies like BD process clinical data that cannot leave controlled environments.
The AI features these companies are adding are specific and practical: intelligent classification of IT tickets with documented decision rationale, anomaly detection in developer workflows with an explainability layer, and clinical data summarization with human-review gates before any output reaches a clinician. None of these require a full product rebuild; all of them require compliance architecture that most AI consultants skip.
We work with your existing compliance posture, not against it. If your security team needs a data flow diagram before approving the AI integration, we produce one. If your legal team needs a documented intended use statement, we draft it with you.
Adding AI-powered ticket classification and priority scoring to IT service management SaaS, with documented decision rationale that satisfies ISO 27001 audit requirements.
Embedding AI anomaly detection into developer workflow tools where ITAR-adjacent code may be present, using on-premises inference that does not send data to third-party API endpoints.
Adding AI summarization to clinical data software with human-review gates, FDA intended use documentation, and audit logs that satisfy clinical quality system requirements.
Pricing depends on feature scope, compliance requirements, and data infrastructure complexity.
A written spec that documents intended use, risk classification awareness, audit requirements, and the human-review gates needed before the feature ships.
The AI feature integrated into your existing codebase with full audit logging, explainability outputs, and tenant-level toggle controls.
Append-only AI output audit logs with structured schema, queryable by your compliance team or your customers' auditors.
Plain-language rationale attached to every AI classification or recommendation output, formatted for display in your product UI.
A documented data flow diagram showing exactly what data moves where during AI inference, suitable for security team review or customer due diligence.
Runbooks for operating the AI feature, compliance documentation templates, and a guide for your team to tune and maintain it after we hand off.
Every AI inference call gets a structured log entry: input hash, model version, prompt version, output, timestamp, and the user or process that triggered it. Logs are append-only and stored separately from your application database so they survive incidents. Your compliance team can pull a complete audit trail for any AI-generated output with a single query.
Most SaaS products in the medical space are not themselves Software as a Medical Device, but they may feed data into clinical decisions or be used by clinicians. We help you identify where your AI outputs sit on the FDA risk ladder, document your intended use accurately, and build the logging and human-review gates that keep you out of the highest-risk SaMD categories. We are not a regulatory affairs firm; we build the software side correctly and connect you with regulatory counsel when needed.
Yes, and we design for this from the start. Every AI feature ships with tenant-level and user-level toggle controls so your enterprise customers can disable AI outputs for their account without affecting other tenants. This is not an afterthought; it is part of the feature spec. Regulated industry buyers frequently require it before signing.
For tools where export-controlled data may be present, we design AI features that run inference on infrastructure you control, using open-weight models deployed in your own cloud environment. We avoid sending data to third-party AI API endpoints. This adds cost and complexity, and we will be direct with you about both before scoping.