What is privacy-compliant AI consulting?

It is designing and building AI so that privacy rules are part of the system, not a policy added later. That means scoped data access, logs of what the system saw and said, clear rules for when a person takes over, and hosting that keeps data where it must stay.

Do you certify that our AI system is compliant?

No. Compliance is your decision and your regulator's. We build to the requirements your privacy or compliance lead sets, document how the system meets each one, and give your team the logs and tests to show it. We sign NDAs and BAAs as standard practice.

Which privacy rules can you build for?

We design for the rules your team works under, such as GDPR, UK GDPR, HIPAA, GLBA or CCPA. Each rule becomes a concrete control, such as what data the bot can read, how long logs are kept, and where the system runs.

Can the AI run inside our own cloud or on-premise?

Yes. We deploy in your own cloud tenant, on-premise, or air-gapped, so conversations and documents stay with you. Hosted or self-hosted models are both options, and the choice affects scope and cost.

How much does privacy-compliant AI consulting cost?

Projects start from $20K, scope-driven. Five things set the price: how long the build runs, its features and integrations, the size of the team, your timeline and priority, and the level of polish, including audit needs. Calls and scoping are free until you get a proposal.

How long does a regulated support build take?

Usually 4 to 5 months from kickoff to production. The first month goes on the data and escalation design with your compliance lead, because that is what lets the rest move quickly.

Privacy-Compliant AI Consulting

Privacy-compliant AI for regulated customer support built and handed over by EdgeFirm

We design and build AI for support teams in insurance, legal, finance and health administration. The bot sees only the data a question needs, every access is logged, and set topics always go to a person. It runs in your cloud or on your own servers, and you own the code.

Book Strategy Call See the Support Case Study

Engagement · Scope

EdgeFirm ships in 4 to 5 months. Fixed price.

What you get

Data access scoped by role
Every answer logged with its sources
Human escalation with full context
Runs in your cloud or on your servers

Timeline

4 to 5 months

Typical scope

1 to 5 integrations

Payback

Modeled before you sign

Privacy rules designed in week oneIncluded

Human escalation built inIncluded

You own the codeIncluded

Audit log entry

Question: Is my claim covered?
Data accessed: Claim status only
Sources cited: Policy terms, section 4
Outcome: Answered, logged

Every answer leaves a record

Sources and data accessed are logged, so you can show an auditor what happened. Example entry.

Why it matters

Why AI Support Stalls in Regulated Teams

Most pilots stop at the same four questions from legal and compliance:

What Personal Data Can the Bot See?

A bot with broad access to customer records is hard to defend. Access has to be scoped to the question and to the person asking.

When Does a Person Take Over?

Complaints, denials and anything legal or medical need a human. If the handoff rules live inside a prompt, nobody can prove they run.

Can We Show What Happened Later?

An auditor or regulator may ask why the bot said something. Without logs of each answer, its sources and its data access, there is no answer.

Where Does the Data Go?

Many tools send conversations to a shared vendor platform. Regulated teams often need the system inside their own cloud or servers.

What we build

What Privacy-Compliant AI Consulting CoversFour parts of every regulated support build, designed before any code is written:

1 · Data Access Design

The Minimum Data for Each Question

Access scoped by role and by question, following each person's existing permissions
Personal data kept in your systems, with references instead of personal details in logs and tickets
Delivered: a lending platform where logs, error reports and support tickets carry references instead of patient details
Your privacy or compliance lead signs off the design before the build

2 · Escalation Workflows

Clear Rules for When a Person Steps In

Topics that always go to a person, agreed with your team in discovery
Handoff on low confidence or when the user asks for a human
Full conversation history carried into the handoff
Delivered: a WhatsApp support assistant in English and Arabic with handoff to an agent

3 · Multi-Agent Support Workflows

Narrow Jobs, Hard Rules Outside the Model

One agent finds the source, another checks the answer, another screens for out-of-scope data
Hardcoded guardrails that run no matter what an agent decides
Delivered: a multi-agent system with a security analyst agent and per-role access for a retail operator
Start with one chatbot and add agents once the basics work

4 · Audit Trail and Deployment

Proof of What the System Did

Every answer logged with its sources and the data it accessed
Deploy in your own cloud tenant, on-premise, or air-gapped
Evaluation set built from your own questions and run before launch
NDAs and BAAs signed as standard practice

What we measure

What EdgeFirm measuresset against your baseline in discovery

Every engagement is scoped to a fixed price and an outcome you can measure, not billable hours.

Support

About 90%

of queries automated at Eona.ae

Result

Legal guidance

50,000+

consultations in year one at InPro

Result

Delivery

4 to 5 months

from kickoff to production

ResultSee case studies

The comparison

THE EDGEFIRM DIFFERENCE

Unlike chatbot platforms:

Runs in your cloud or on your servers
You own the code and the data
No per-seat or per-conversation fees

Unlike advisory-only consultancies:

The engineers on your calls write the code
Fixed price for a defined scope
Privacy design and build in one team

Unlike DIY with an API key:

Access scoping and audit logs included
Escalation rules outside the model
Tested on your own questions before launch

Common questions

Common Questions About Privacy-Compliant AI Consulting

: It is designing and building AI so that privacy rules are part of the system, not a policy added later. That means scoped data access, logs of what the system saw and said, clear rules for when a person takes over, and hosting that keeps data where it must stay.
: No. Compliance is your decision and your regulator's. We build to the requirements your privacy or compliance lead sets, document how the system meets each one, and give your team the logs and tests to show it. We sign NDAs and BAAs as standard practice.
: We design for the rules your team works under, such as GDPR, UK GDPR, HIPAA, GLBA or CCPA. Each rule becomes a concrete control, such as what data the bot can read, how long logs are kept, and where the system runs.
: Yes. We deploy in your own cloud tenant, on-premise, or air-gapped, so conversations and documents stay with you. Hosted or self-hosted models are both options, and the choice affects scope and cost.
: Projects start from $20K, scope-driven. Five things set the price: how long the build runs, its features and integrations, the size of the team, your timeline and priority, and the level of polish, including audit needs. Calls and scoping are free until you get a proposal.
: Usually 4 to 5 months from kickoff to production. The first month goes on the data and escalation design with your compliance lead, because that is what lets the rest move quickly.