An AI concept lacks a production path
Translate a promising demonstration into a secure, measurable, observable, and maintainable system.
AI applications, agents, retrieval, automation, models, and data systems
AI development
Dependable AI systems with evaluation, security, observability, and human control built in.
Capability / 01
Built around the operating outcome.
Rokad builds AI-native applications, agents, generative workflows, retrieval systems, machine-learning capabilities, computer-vision systems, model integrations, and MLOps foundations. The focus is measurable utility, controlled behaviour, production reliability, and responsible operation.
01
AI product discovery and feasibility
02
Agent and workflow orchestration
03
RAG, search, and enterprise knowledge systems
04
Model, provider, tool, and application integration
05
Machine-learning and computer-vision pipelines
06
Evaluation, tracing, guardrails, approvals, and MLOps
When to engage / 02
Built for material technology and operating constraints.
AI outputs cannot be trusted operationally
Introduce evaluation, grounding, permissions, policy checks, approval gates, fallbacks, and auditability.
Knowledge and workflows remain inaccessible
Connect models to governed enterprise information, tools, and business processes without surrendering control.
Service scope / 03
From decision through production operation.
Feasibility and system design
Define the task, evidence, model strategy, data requirements, risks, evaluation plan, and human-control model.
AI application engineering
Build interfaces, orchestration, retrieval, tool use, integrations, data pipelines, and operational controls.
Evaluation and operation
Establish quality datasets, automated evaluations, observability, cost controls, release gates, and continuous improvement.
Specialisations / 04
Choose the solution closest to the buying requirement.
01
AI agents
Tool-using agents and governed autonomous workflows with approvals, policy, tracing, and recovery.
02
Generative AI
Text, document, voice, image, and multimodal applications built around controlled business outcomes.
03
RAG and knowledge systems
Grounded retrieval, enterprise search, document intelligence, and private knowledge access.
04
AI integration
Add AI capabilities to existing products, data, workflows, applications, and operations.
05
Machine learning
Prediction, recommendation, classification, forecasting, optimisation, and anomaly detection.
06
Computer vision
Image and video detection, recognition, inspection, extraction, and visual analytics.
07
MLOps
Model deployment, evaluation, observability, governance, versioning, and lifecycle operations.
Use cases / 02
Where this capability creates leverage.
Engagements are structured around measurable technical, operational, product, or commercial outcomes.
Knowledge and research assistants
Provide grounded answers, synthesis, and source access across governed private information.
Controlled workflow automation
Allow AI to analyse, draft, recommend, route, or act through approved tools and business rules.
AI capabilities inside an existing product
Add generation, extraction, classification, recommendation, search, or conversational functionality.
Predictive and visual intelligence
Apply machine learning or computer vision to forecasting, detection, scoring, inspection, and decision support.
Engineering standards / 05
Production quality is not a later phase.
Evaluation before automation
Quality criteria, representative datasets, failure modes, and release thresholds are defined before expanding autonomy.
Control over side effects
Permissions, policy checks, approval gates, idempotency, audit trails, and escalation paths govern consequential actions.
Observable AI operation
Inputs, outputs, retrieval, tool calls, latency, cost, model behaviour, and quality trends are measured appropriately.
Delivery / 03
A controlled path from decision to operation.
Begin with one phase or cover the complete lifecycle under one accountable team.
Discover
Clarify the business objective, users, constraints, dependencies, risks, and measurable acceptance criteria.
Architect
Define the system boundaries, delivery plan, integrations, security controls, and operating model before implementation.
Build and validate
Deliver in controlled increments with review, automated testing, documentation, and stakeholder validation.
Deploy and improve
Launch safely, establish observability and support, then improve the system using operational evidence.
Typical deliverables
AI feasibility and risk assessment
Architecture and model strategy
Production AI application, workflow, or API
Retrieval and data pipelines
Evaluation datasets and quality framework
Guardrails, approvals, tracing, and audit controls
Deployment, monitoring, governance, and operating documentation
Engagement models / 06
A delivery structure for the scope and stage.
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 engineering
Specialist engineers working inside an existing product, technology, or operations team.
Managed evolution
Ongoing maintenance, reliability, security, feature delivery, and roadmap execution after launch.
Related services / 07
Connect the wider technology lifecycle.
01
Software development
Build the product, workflow, platform, and integration layer around AI capabilities.
02
Managed technology services
Operate AI systems, infrastructure, security, observability, and continuous improvement.
03
Technology consulting and research
Assess AI opportunities, vendors, risks, economics, and adoption roadmaps.
FAQ
Questions about ai development.
Scope, ownership, assumptions, and delivery are clarified before work begins.
01How do you decide whether AI is appropriate?
How do you decide whether AI is appropriate?
We evaluate the task, available evidence, error tolerance, workflow impact, cost, latency, privacy, and measurable advantage over deterministic software.
02Can sensitive AI actions require approval?
Can sensitive AI actions require approval?
Yes. We implement permissions, policy checks, approval gates, audit trails, fallbacks, and escalation paths based on action risk.
03Can Rokad use private or self-hosted models?
Can Rokad use private or self-hosted models?
Yes. Model strategy can include managed providers, open-weight models, private infrastructure, or hybrid routing based on security, performance, cost, and operational constraints.
04How is AI quality measured after launch?
How is AI quality measured after launch?
We combine offline evaluation, production telemetry, user feedback, sampled review, failure analysis, and controlled release gates.
05Can you integrate AI into an existing application?
Can you integrate AI into an existing application?
Yes. We can add AI through APIs, background workflows, embedded interfaces, data pipelines, tool integrations, and governed automation without rebuilding the entire product.
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.