Product teams adding AI-native capabilities
Integrate generation, extraction, classification, conversation, search, recommendations, multimodal input, and agent workflows into software products.
Responses API, agents, tools, structured outputs, file search, retrieval, multimodal applications, evaluations, and production controls
OpenAI integration and development services
Rokad builds production AI applications and integrations with OpenAI APIs across agents, tools, retrieval, structured outputs, multimodal workflows, evaluations, and observability.
Platform fit / 01
Designed for teams with a specific platform requirement.
OpenAI APIs can support assistants, agents, extraction, generation, search, multimodal interfaces, and tool-enabled workflows. Rokad engineers the surrounding application, data, permissions, evaluation, fallback, cost, latency, audit, and operational controls required for dependable business use.
Enterprises connecting AI to private knowledge and tools
Build governed retrieval, file workflows, authorised actions, approval gates, audit, and identity-aware applications.
Teams moving prototypes into production
Introduce evaluations, structured outputs, observability, provider abstractions, cost controls, security, and release discipline.
Implementation risks / 02
The platform problems Rokad is prepared to solve.
A prompt demo lacks an application contract
Inputs, outputs, schemas, tools, data, failure, confidence, user experience, and escalation are not defined.
Tool-enabled agents can create unintended changes
Permissions, approval, idempotency, validation, audit, rate limits, and recovery are missing around application actions.
Model updates and variability affect production quality
Representative evals, version tracking, regression thresholds, fallback, and release controls are not established.
Platform capabilities / 03
What Rokad can implement and operate.
01
OpenAI API strategy, task design, model selection, architecture, and feasibility
02
Responses API integration, streaming, conversation state, structured outputs, and function tools
03
Agent workflows, tool orchestration, approvals, application actions, and multi-step execution
04
File search, vector stores, embeddings, retrieval, document processing, and private knowledge
05
Text, image, audio, file, and multimodal product experiences
06
Prompt, tool, retrieval, safety, latency, cost, and agent evaluation systems
07
Tracing, monitoring, quotas, caching, fallbacks, provider abstraction, security, and managed operation
Implementation system / 04
The architecture behind a dependable platform delivery.
AI application contract
Tasks, inputs, context, outputs, schemas, tools, permissions, uncertainty, user control, and failure behaviour.
OpenAI integration layer
Responses, tools, streaming, structured output, files, retrieval, model routing, rate limits, and state.
Knowledge and action system
Documents, embeddings, retrieval, citations, functions, APIs, approvals, audit, idempotency, and recovery.
Evaluation and operation
Datasets, graders, regression, traces, latency, cost, quality, safety, versions, incidents, and release gates.
Use cases / 05
Where this platform creates practical leverage.
AI product assistant
Provide contextual conversation, product actions, account workflows, recommendations, and escalation inside an application.
Document intelligence
Extract structured information, classify files, summarise content, validate fields, route review, and update systems.
Enterprise knowledge agent
Retrieve governed private information, cite sources, synthesise answers, and use authorised tools under policy.
AI workflow automation
Analyse requests, draft outputs, call business APIs, request approval, record evidence, and recover from failure.
Architecture / 06
Platform-specific engineering decisions and boundaries.
Structured output at system boundaries
Use explicit schemas and validation when model output drives databases, APIs, workflows, decisions, or user interfaces.
Tools expose narrow authorised capabilities
Define scoped functions with validated arguments, identity context, policy, approval, idempotency, and audit.
Evaluation controls model change
Run representative tests across prompts, tools, retrieval, models, latency, cost, and safety before production release.
Quality and governance / 07
Production controls are part of the implementation.
Evaluated behaviour
Representative datasets, task criteria, failure modes, model comparisons, and release thresholds are defined before production expansion.
Governed model access
Identity, data boundaries, tool permissions, moderation, approvals, audit, retention, and provider controls match the use case.
Provider-aware operation
Models, prompts, tools, latency, cost, quotas, versions, fallbacks, telemetry, and migration risk are monitored explicitly.
Delivery / 08
A controlled path from assessment to operation.
Assess
Clarify the business outcome, current systems, platform constraints, data, integrations, risks, ownership, and measurable acceptance criteria.
Design
Define the platform architecture, workflow or storefront model, extensions, integrations, security, environments, and migration sequence.
Implement and validate
Build in controlled increments with testing, stakeholder review, observability, documentation, and platform-specific quality controls.
Launch and operate
Deploy safely, transfer ownership, monitor production behaviour, support users, and improve the implementation using operational evidence.
Typical platform deliverables
OpenAI use-case, data, model, security, cost, and risk assessment
Application, agent, tool, retrieval, evaluation, and operating architecture
Production OpenAI API integrations, agents, structured workflows, and user experiences
Knowledge, file, retrieval, tool, approval, audit, and application integrations
Evaluation datasets, regression tests, tracing, monitoring, cost, and release controls
Developer, product, operator, governance, support, and handover documentation
Engagement models / 09
Use the delivery structure that matches the platform work.
Assessment and roadmap
A bounded review of the current platform, requirements, gaps, risks, architecture, and an executable next-stage plan.
Fixed-scope implementation
A defined integration, migration, storefront, application, workflow, or platform outcome with explicit acceptance criteria.
Embedded platform specialists
Specialists working alongside internal product, engineering, operations, marketing, data, or enterprise teams.
Managed platform evolution
Ongoing maintenance, releases, integrations, support, optimisation, governance, and roadmap execution after launch.
Related platforms and services / 10
Compare adjacent platforms or continue into the wider system.
01
Anthropic Claude
Claude API integration for reasoning, long-context work, tool use, and governed agents.
02
Microsoft Foundry
Enterprise AI applications, models, agents, evaluations, tracing, and Azure governance.
03
Amazon Bedrock
Managed foundation models, agents, knowledge bases, guardrails, and AWS integration.
01
AI development
AI applications, agents, retrieval, evaluation, model integration, and intelligent workflows.
02
Software development
Custom applications, backends, integrations, APIs, marketplaces, and enterprise systems.
03
Data engineering
Pipelines, platforms, warehouses, analytics engineering, BI, and governed data operations.
FAQ
OpenAI integration and development services
Platform scope, ownership, licences, data, integrations, security, migration, and long-term operation are clarified before delivery.
01Can Rokad integrate OpenAI into an existing product?
Can Rokad integrate OpenAI into an existing product?
Yes. We can add API-backed features, background workflows, agents, retrieval, structured extraction, multimodal experiences, and governed actions without rebuilding the entire product.
02Can OpenAI use our private documents?
Can OpenAI use our private documents?
Yes, through an architecture that defines data selection, upload or retrieval, permissions, indexing, retention, citations, audit, and deletion according to requirements.
03How is OpenAI output quality measured?
How is OpenAI output quality measured?
We create representative datasets and evaluate task success, structure, retrieval, tool use, safety, latency, cost, and business-specific failure modes.
04Can Rokad support more than one AI provider?
Can Rokad support more than one AI provider?
Yes. We can design provider abstractions, routing, fallbacks, evaluation, data boundaries, and migration paths where resilience or model choice justifies it.
OpenAI · AI integration services
Move from an OpenAI demo to an evaluated production system.
Rokad can build the application, tools, retrieval, evaluations, controls, and operational model around OpenAI APIs.
Contact / 05
Bring us the difficult technology problem.
Tell us what you need to build, improve, procure, deploy, or operate. We will respond with a practical next step.