Technology
OpenAI
Add OpenAI-powered features safely—assistants, automations, and copilots built with guardrails, evaluation, and monitoring for reliability.
Best For
Ideal use cases
Applications needing natural language processing
Projects requiring text generation
Chatbots and conversational interfaces
Content generation features
Applications with language understanding needs
What We Build
Projects we deliver
AI-powered chatbots
Content generation tools
Text summarization features
Language translation applications
Code generation assistants
Email and document analysis
Creative writing tools
Customer support automation
Ecosystem
Compatible tools & integrations
Seamless Integrations
Works with your existing stack
Use Cases
Recommended use cases
Customer support chatbots
Content generation platforms
Writing assistants and tools
Applications needing language understanding
Prototypes testing AI capabilities
Delivery
How we deliver
We integrate OpenAI APIs with proper error handling and rate limiting
Implement streaming for better user experience
Use function calling for structured outputs and tool use
Set up proper prompt engineering and context management
Implement cost monitoring and optimization
FAQ
Frequently asked questions
We optimize costs by caching responses, using appropriate models (GPT-3.5 for simple tasks, GPT-4 for complex), implementing rate limiting, and monitoring usage. We set up billing alerts.
Yes. We can fine-tune GPT-3.5 models for specific use cases. Fine-tuning improves accuracy for domain-specific tasks but requires training data and additional costs.
We implement rate limiting, retry logic with exponential backoff, and request queuing. We also use caching to reduce API calls and monitor usage to stay within limits.
AI
Add AI on top of this stack
Two common AI services that pair well with this technology, plus the evidence-first buyer sprint.
Related
Explore related technologies
Regional
Delivery considerations for your region
Data and risk discovery (United Kingdom)
Privacy, security, residency, and regulatory requirements differ by workflow. We document the applicable data flows, roles, retention needs, and control owners before recommending an architecture.
The resulting proposal lists the controls and evidence that are actually in scope. It is not a generic compliance, certification, or legal-assurance promise.
- Map data sources, destinations, roles, and sensitive fields
- Record access, retention, logging, and deletion requirements
- Identify required security or procurement evidence before contracting
- Use an NDA or DPA only when the parties mutually execute it
Working model (United Kingdom)
Exact live-overlap hours, response expectations, meeting windows, and escalation contacts are confirmed in the proposal for each engagement.
Written decisions, scoped milestones, and asynchronous updates reduce unnecessary meetings without implying an unagreed service level.
- Proposal-specific overlap and meeting windows
- Named owners for decisions and blockers
- Written scope, assumptions, and change decisions
- Milestone cadence agreed before kickoff
Commercial setup (United Kingdom)
The contracting entity, proposal currency, invoicing cadence, payment terms, intellectual-property terms, and required vendor documents are agreed before work begins.
The Opportunity Sprint can establish the evidence needed to scope a production pilot; it does not pre-commit either party to a rollout.
- Contracting entity and currency confirmed in writing
- Milestones and acceptance criteria defined in the proposal
- Vendor-document requirements identified before signature
- Scope changes require an explicit written decision
Delivery controls (United Kingdom)
Testing, observability, release, security, and handover controls are selected for the actual system risk rather than promised as a generic bundle.
Acceptance measures and production responsibilities are recorded before implementation so both teams know what evidence will support release.
- Risk-based testing and acceptance measures
- Release, rollback, and observability responsibilities
- Security controls tied to the agreed threat model
- Handover artifacts defined in the signed scope
Want to scope this properly?
Planning an OpenAI integration? Share your use case and we’ll recommend a safe rollout plan with clear milestones. GBP-based engagements.
We’ll review the context and reply with a practical next step.