Technology
Run AI on your own code, documentation and infrastructure.
Technology companies want to use AI with their source code, product documentation and support knowledge, but often cannot send them to third-party services. Private and dedicated AI environments give you more control over where models run and what they can see.
Use cases
Key AI use cases
Use cases for engineering, product and support teams.
Code and repository assistant
Ask questions about your codebase and internal libraries, with answers that point to the relevant files.Documentation search
Search architecture notes, runbooks and product documentation from one place.Support knowledge
Help support teams draft answers from product documentation and resolved tickets.Self-hosted models for products
Host models on infrastructure dedicated to your workloads, for internal tools or product features.
Who it helps
Built around the people doing the work
Who it helps
- Software engineers and platform teams
- Support and customer success teams
- Product managers
- CTOs and engineering leaders
Challenges we often hear
- Source code and customer data that cannot leave your control
- Knowledge scattered across wikis, tickets and repositories
- Unpredictable costs from usage-based AI services
- Customer security questionnaires that ask where data goes
How it works
From a support ticket to a sourced reply
An example of a support team using a private assistant.
Your information
- Code repositories
- Architecture notes and runbooks
- Product documentation
- Resolved support tickets
Private AI environment
- Hosted in an environment you control
- Searches only what each user may access
- Shows the sources behind each answer
What your team gets
- Answers with links to files and pages
- Draft support replies
- Model endpoints for your own tools
Index the knowledge
Product documentation and resolved tickets are indexed in your environment.
Ticket arrives
A support engineer opens a new customer question.
Find similar cases
The assistant finds related documentation and previously resolved tickets.
Draft a reply
It drafts a reply with links to the sources it used.
Engineer responds
The engineer checks the draft, adjusts it and replies to the customer.
Benefits
What it can mean for your organisation
The outcomes we design for. Results depend on your data, people and processes, and are agreed with you rather than assumed.
Control over your IP
Code and documentation stay in an environment you control.
Faster onboarding
New engineers can ask questions about systems and code.
Predictable infrastructure
Capacity planned around your workloads, not per-request pricing.
Clearer security answers
A documented environment makes it easier to explain where data goes.
Capabilities
AI capabilities and recommended models
AI capabilities
- Question answering across code and documentation
- Drafting support replies from known answers
- Summarising incidents and long threads
- Hosting models behind your own internal APIs
- Sizing GPU and storage capacity for AI workloads
Recommended model types
Code-capable language model
Hosted in your environment for questions about code and technical documentation.
Embedding model for search
Indexes repositories, documentation and tickets for retrieval.
Open-weight models you choose
Where you need them, models selected and hosted on dedicated infrastructure for your own products.
Specific models are chosen during design based on your data, languages, accuracy needs and infrastructure, and explained in plain language.
Integrations
Works alongside your existing systems
Typical systems we plan around. Integrations depend on the systems you use and the access they allow, and are confirmed during design.
- Source code hosting
- Issue trackers and support desks
- Wikis and documentation platforms
- Chat and collaboration tools
- Identity and single sign-on
- Monitoring and logging
Security & compliance
Security, privacy and compliance
Security is considered throughout design, deployment and operation, and discussed openly.
In every environment
- Designed so your data is not shared with public AI services
- Access tied to your own users and permissions
- Hosting location agreed with you
- Configuration documented, so it is never a black box
Considerations for Technology
- Source code and customer data kept out of third-party AI services
- Access through your identity provider and existing groups
- Network isolation and hosting location agreed in the design
- Documentation to support customer security reviews and your own frameworks, such as ISO 27001 or SOC 2
Compliance stays with you and your advisers
Case studies
Case studies in Technology
No published case studies yet
We publish case studies only for completed projects that clients have approved for publication. There are none for Technology yet. We are happy to talk through how a project could be scoped.
Discuss a projectRelated solutions
Plan AI infrastructure for your team
Share your workloads and constraints, and we will help you decide between private, managed and dedicated options.
