Product teams adding AI without rebuilding the platform
Introduce focused capabilities through modular services, APIs, background workflows, and embedded interfaces.
Add governed AI capabilities to existing products, workflows, data, and operations
AI integration services
Rokad integrates AI into existing software and business workflows through secure APIs, interfaces, data pipelines, evaluations, and operating controls.
Designed for / 01
A focused delivery model for the organisations that need it.
AI integration should improve a specific user or operational outcome without destabilising the surrounding system. Rokad connects models, retrieval, agents, extraction, generation, classification, recommendation, and automation to existing products, data, applications, and approval workflows.
Operations teams connecting AI to existing tools
Use AI across CRM, support, documents, communication, knowledge, finance, or internal applications with human control.
Organisations consolidating AI experiments
Replace isolated pilots with shared model access, governance, observability, evaluation, and reusable platform components.
Challenges / 02
The problems this service is built to solve.
The AI demo is disconnected from the workflow
Users must manually move context and outputs between systems, reducing adoption and increasing error.
Existing architecture was not designed for AI
Long-running work, probabilistic outputs, provider limits, evaluation, context, and feedback require new system patterns.
Multiple teams use models without common controls
Credentials, data, prompts, providers, cost, quality, logging, and release behaviour are inconsistent.
Capabilities / 03
What Rokad can deliver.
01
AI opportunity and workflow integration assessment
02
Model-provider, open-weight, private, and hybrid integration
03
Generation, extraction, classification, recommendation, search, and agent APIs
04
Product UI, background jobs, events, queues, and human-review workflows
05
CRM, support, document, communication, data, and enterprise-system integration
06
Shared model gateway, routing, policy, cost, logging, and evaluation services
07
Security, observability, rollout, fallback, and managed operation
Platform expertise
Platform-specific implementation services.
01
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.
02
Anthropic Claude integration services
Rokad builds production applications and agent workflows with Anthropic Claude across tool use, long-context analysis, retrieval, MCP, structured outputs, and evaluation.
03
Google Gemini integration services
Rokad builds Gemini-powered applications across multimodal understanding, function calling, structured outputs, grounding, agents, Vertex AI, and production evaluation.
04
Microsoft Foundry development services
Rokad develops enterprise AI applications with Microsoft Foundry across models, agents, tools, evaluations, tracing, monitoring, security, data, and Azure integration.
05
Amazon Bedrock development services
Rokad develops enterprise generative AI applications on Amazon Bedrock across foundation models, agents, knowledge bases, guardrails, evaluations, and AWS integration.
06
Hugging Face development services
Rokad develops AI systems with Hugging Face across model discovery, evaluation, fine-tuning, datasets, inference endpoints, custom deployment, applications, and lifecycle operations.
Solution components / 04
The system behind the visible product.
Workflow insertion point
Define where AI receives context, supports a decision, creates an output, requests review, or triggers an approved action.
AI service boundary
Provide stable contracts for model access, retrieval, prompts, tools, validation, logging, cost, and provider change.
Human and deterministic controls
Keep policy, permissions, business invariants, approvals, validation, and high-risk decisions outside unconstrained generation.
Adoption and operation
Measure quality, user behaviour, time saved, exceptions, cost, latency, failures, and workflow impact after rollout.
Use cases / 05
Where this capability creates practical leverage.
AI inside a SaaS product
Add assistance, extraction, generation, search, recommendation, or automation to an existing customer workflow.
Document processing integration
Receive files, extract and validate data, request review, update systems, and preserve evidence and audit history.
Customer-service augmentation
Ground answers, summarise interactions, recommend actions, draft responses, and automate approved service steps.
Enterprise AI gateway
Provide reusable provider access, routing, policy, telemetry, cost, and evaluation capabilities across teams.
Architecture and integration / 06
Designed to fit the wider technology environment.
Loose coupling
Expose AI through stable service contracts so models, providers, prompts, and retrieval can evolve without rewriting the product.
Asynchronous execution
Use queues, durable workflows, callbacks, status, cancellation, and retries for long-running or rate-limited tasks.
Progressive rollout
Introduce shadowing, assistance, optional use, approval, limited cohorts, and monitored expansion before deeper automation.
Quality and control / 07
Production requirements are part of the build.
Measured behaviour
Representative evaluation data, quality criteria, failure modes, and release thresholds are defined before expanding production use.
Controlled actions
Permissions, policy checks, approval gates, audit trails, fallbacks, and escalation paths govern consequential AI behaviour.
Observable operation
Inputs, outputs, retrieval, tool calls, latency, cost, model versions, and quality trends are monitored appropriately.
Delivery / 08
A controlled path from requirement to operation.
Discover
Clarify the business outcome, users, workflows, constraints, dependencies, risks, and measurable acceptance criteria.
Architect
Define the system boundaries, data, integrations, security, operating model, delivery sequence, and technical decisions.
Build and validate
Deliver in controlled increments with stakeholder review, automated testing, documentation, and production-quality engineering.
Deploy and improve
Launch safely, establish observability and support, then improve the system using operational evidence and user feedback.
Typical deliverables
AI integration opportunity, workflow, and risk assessment
Model, service, data, interface, and control architecture
Production AI service, API, product interface, and system integrations
Validation, approval, fallback, security, and audit controls
Evaluation, telemetry, cost, latency, and rollout dashboards
Technical, operator, governance, and handover documentation
Engagement models / 09
Use the delivery structure that matches the work.
Fixed-scope delivery
A defined outcome, scope, acceptance criteria, milestones, and commercial structure for a bounded project.
Dedicated product team
A stable cross-functional team delivering an evolving roadmap with shared product and engineering ownership.
Embedded specialists
Specialist engineers working inside an existing product, technology, data, design, or operations team.
Managed evolution
Ongoing reliability, security, maintenance, feature delivery, and roadmap execution after launch.
Related capabilities / 10
Continue through the wider product and technology system.
FAQ
AI integration services
Scope, ownership, assumptions, delivery, security, and long-term operation are clarified before work begins.
01Can AI be added without rebuilding our application?
Can AI be added without rebuilding our application?
Usually yes. We can introduce modular AI services, APIs, background workflows, event handlers, embedded interfaces, and review queues around the existing product architecture.
02Can we change model providers later?
Can we change model providers later?
Yes. We can separate provider-specific behaviour behind a model or AI-service boundary, although capabilities and output behaviour still require evaluation when providers change.
03How do you roll out AI safely to existing users?
How do you roll out AI safely to existing users?
We can use internal testing, shadow execution, optional assistance, review-required outputs, limited cohorts, feature flags, quality thresholds, monitoring, and rollback.
04Can one integration support several AI use cases?
Can one integration support several AI use cases?
Yes. Shared model access, retrieval, policy, telemetry, evaluation, and cost components can support multiple product and workflow capabilities while keeping task-specific logic separate.
AI development
Add AI where it improves the existing product or workflow—not where it creates new fragility.
Rokad can identify the integration boundary, engineer the capability, and establish evaluation and operating controls.
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.