AI your compliance team can sign off.
Sensitive data and AI can work together when privacy is designed in from the start. We build PII detection and redaction, prompt-injection defence and deployments that keep data where your rules say it must stay.
When you need it.
Safety work added at the end is expensive and incomplete. Built in from the first sprint, it shapes the design instead of blocking the launch.
- Personal data in promptsNames, IDs and health or financial details go to a model without control.
- A compliance review is blocking launchLegal and security can’t approve what they can’t see.
- Worry about prompt injectionUsers or documents could trick the system into ignoring its rules.
- Data that can’t leave the buildingRegulations or contracts rule out sending data to a public cloud API.
How it works.
The shape of what we build, from your data to the people who use it.
- InputUser request or document
- Detect & redactPII removed or masked
- GuardInjection and policy checks
- ModelWhere your rules allow
- AuditWhat was sent, by whom
What we build.
The parts of a production system, each one tested and handed over.
PII detection and redaction
Personal data found and masked before it reaches a model or a log.
Prompt-injection defence
Checks that stop user input or documents from overriding the system’s instructions.
Output checks
Rules that block answers containing data or content they must not.
Audit trails
A record of what was sent, what came back and who asked.
Private deployment
On-prem or air-gapped deployment with self-hosted models when data must stay inside.
Safety evals
Red-team cases kept in the eval suite so protections are tested on every release.
What you get.
- Redaction of personal data before it reaches a model
- Injection and output checks in front of the model
- An audit trail your reviewers can read
- A deployment that matches your data rules
- Safety tests that run on every release
How we run it.
Start with a short discovery sprint and decide with real evidence.
- Step 1
Discovery sprint
2 weeks
We map the problem, look at your data and systems, and agree what “good” means as a real eval set.
- Step 2
Build
Working version early
We build the first working version in your environment, with tests and monitoring from the start.
- Step 3
Harden
Before real users
Latency, cost, failure handling and security, then a runbook your team can follow.
- Step 4
Pilot and run
Real users
A pilot with real users and weekly reviews, then ongoing improvement if you want us to stay.
What we build with.
Model- and provider-agnostic: we pick what fits your latency, cost and compliance needs.
- Safety
- PII detection & redactionPrompt-injection defenceAudit trailsRed-team prompts
- Deployment
- On-premAir-gappedLlama (self-hosted)Mistral (self-hosted)
Frequently asked questions.
Still unsure? A 30-minute call with a tech lead answers the rest.
Ask us directlyCan we use AI with sensitive data at all?
Often, yes, with redaction, access controls, audit trails and the right deployment. We design these in from the start.
What is prompt injection?
Text in a user message or a document that tries to make the AI ignore its instructions. We add checks for it and keep attack examples in the test suite.
Can the model run inside our network?
Yes. Self-hosted models such as Llama and Mistral can run on-prem or in an air-gapped environment.
What will our compliance team get to review?
A description of the data flow, what is redacted and where, the checks in place, and audit logs showing what the system did.
Let’s talk about your project.
Tell us about your team, your stack and the problem you want solved. We’ll come back with how we would approach it.
Response time
Within one business day
Prefer a call?
Headquarters
Charlotte, North Carolina
Thank you. Your message has reached our team.
A member of our team will reply, usually within one business day. Your reference is .