Private GPT assistants on your data, your infra, your terms
Trained on your SOPs, your knowledge base, your brand voice. Runs where your security team is comfortable — your cloud, your audit logs, your access controls.
Why companies build their own GPT
Public ChatGPT and Claude are remarkable products, but they live in someone else’s data centre, train on aggregated traffic, and answer with the entire internet as context. For legal, HR, finance, regulated industries — and any company that takes data-residency seriously — that’s a non-starter.
A custom GPT assistant inverts the model. It runs in your environment, retrieves only from your approved sources, refuses out-of-scope queries, and logs every interaction for audit. Same conversational interface; very different governance posture.
What “enterprise-grade GPT” actually requires
These are the features that decide whether legal will sign off.
Data sovereignty
Runs in your tenancy, your region, your VPC. Data never leaves your control. No vendor model training on your prompts.
Full audit logging
Every prompt, every response, every retrieval source — logged with user identity and timestamp. Exportable to SIEM.
Role-based access (RBAC)
Engineering team sees code docs, HR team sees policies, legal sees contracts. Same assistant, different visibility per role.
Refusal & PII guardrails
Refuses out-of-scope queries, redacts PII before storage, blocks prompt-injection patterns. Tested against a 200-case red-team set.
Model choice
Claude Sonnet, GPT-4, or open-weight Llama / Mistral on your hardware. We benchmark per use case; you pick on cost / quality / control.
Adoption analytics
See which teams use it, which questions saturate, where it refuses too aggressively. Drives the next round of content + tuning.
Three places a private GPT earns its keep
Patterns where in-house knowledge is the bottleneck.
HR + Policy assistant
- Answers travel, benefits, leave, IT-policy questions from policy docs
- Reduces repetitive HR-inbox load 40-60% within 90 days
- Cites the policy section in every answer
Contract / legal lookup
- Searches contracts, SOWs, NDAs for clauses and deviations
- Flags non-standard terms vs. your playbook
- Audit log proves who asked what, when
Engineering knowledge base
- Indexed on your repos, RFCs, runbooks, on-call playbooks
- Cuts new-engineer onboarding time meaningfully
- Answers "where is the X service" without pinging staff engineers
From data inventory to production
Heavier on governance, lighter on flash. The boring work is where this lives or dies.
Week 1 · Data inventory + access
Catalogue every source. Decide what's in / out. Lock down access roles. Sign DPAs. Boring; non-negotiable.
Week 2 · Index + guardrails
Embed + index approved sources. Write refusal rules. Red-team against a starter prompt-injection set.
Week 3 · RBAC + audit pipeline
Wire SSO. Map roles to source visibility. Pipe audit logs into your SIEM / data lake.
Week 4-5 · Pilot + scale
Pilot with one team (typically HR or engineering). Measure adoption, refine, expand to the rest of the company.
What security + legal ask first
These are the questions that decide whether the project is signed.
01 Will our prompts or documents train someone else's model?
No. We use Anthropic / OpenAI / Azure enterprise endpoints (zero-retention by contract) or self-hosted open-weight models on your hardware. Either way, no third party trains on your data.
02 Where does the data physically live?
Your region, your tenancy. AWS / Azure / GCP regions of your choice. Self-hosted is also an option if your industry requires it (defence, healthcare, certain financial services).
03 How do we prove what the assistant said to whom?
Full audit log per query: user, timestamp, prompt, response, retrieved sources. Exportable to your SIEM. Retention configurable to match your records-management policy.
04 What stops someone using prompt injection to leak data?
A red-team pass before launch + ongoing monitoring. Retrieval is RBAC-scoped (the model only sees what the user is allowed to see), and refusal rules block known injection patterns. It’s not unbeatable, but it raises the bar significantly.
05 Open-weight model or commercial API — which is right for us?
Depends. Commercial APIs (Claude, GPT) give better quality and lower ops burden. Open-weight (Llama, Mistral) gives zero external calls and unlimited customisation. We benchmark both on your real questions before recommending.
06 What about SOC 2 / ISO 27001?
The architecture supports both. We’ll work with your auditor to map controls (access management, logging, data flow, key management) so the deployment fits your existing certifications.
What teams say after going live with a custom GPT assistant
Tell us about your data, regulations, and team
Two-minute form. We reply within 4 working hours.