Services
AI engineering services, built for production
Six service areas covering how AI agents are designed, integrated, shipped, and operated.
Custom AI Agent Development
We design agents that plan, call tools, and complete multi-step tasks inside your existing systems. Each agent starts from a narrow, testable job definition rather than an open-ended chatbot.
Business problems addressed
- Repetitive knowledge work that needs judgment, not just rules
- Requests spread across email, tickets, and internal tools
- Prototype agents that behave inconsistently outside a demo
Technical capabilities
- Tool and function calling against internal and third-party APIs
- Retrieval-augmented generation over structured and unstructured data
- Structured outputs with schema validation and retries
- Guardrails, permission scoping, and escalation to humans
- Model selection and routing across hosted and open-source LLMs
Example use cases
- Triage and drafting agent for inbound requests
- Research agent that gathers and summarises source material
- Back-office agent that updates records after verification
Multi-Agent Systems
Some workflows genuinely benefit from several specialised agents. We model them as explicit graphs with typed state, bounded loops, and clear ownership of each step.
Business problems addressed
- Single prompts that grow too large to maintain or evaluate
- Tasks that mix retrieval, analysis, and action with different risk levels
- Agent loops that are hard to debug or cost-control
Technical capabilities
- Graph-based orchestration with checkpointed, resumable state
- Supervisor, router, and worker agent patterns
- Per-agent tool permissions and budget limits
- Deterministic fallbacks for steps that do not need an LLM
- Trace-level observability across agent hand-offs
Example use cases
- Analyst, reviewer, and writer agents producing a vetted report
- Intake router that dispatches to domain-specific agents
- Planner and executor agents with a human approval gate
AI Workflow Automation
We replace brittle rule chains and manual hand-offs with workflows where AI handles the ambiguous steps and conventional code handles everything that must be exact.
Business problems addressed
- Manual copy-and-paste between systems
- Unstructured inputs such as emails and PDFs that block automation
- Rules engines that break whenever input formats change
Technical capabilities
- Document and email parsing into validated structured data
- Queue-based, idempotent job processing with retries
- Human-in-the-loop review steps and approval routing
- Webhook, API, and scheduled triggers
- Audit trails for every automated decision
Example use cases
- Inbound document intake and routing
- Ticket classification and enrichment
- Scheduled data reconciliation with exception review
AI SaaS Product Development
We build AI-native products with the same discipline as any serious software product: clear domain models, tested APIs, usable interfaces, and cost-aware inference.
Business problems addressed
- Turning an AI idea into a maintainable product, not a demo
- Unpredictable inference cost and latency at scale
- Needing a full-stack team that understands both product and AI
Technical capabilities
- Full-stack delivery: web front end, APIs, data layer, and infrastructure
- Multi-tenant architecture, role-based access, and usage metering
- Streaming interfaces and asynchronous long-running tasks
- Prompt, model, and cost management as versioned configuration
- CI/CD with automated evaluation gates
Example use cases
- Vertical SaaS with an embedded assistant
- Internal tools that expose agents through a reviewed UI
- API products that wrap an agent behind a stable contract
Enterprise AI Integration
Value usually comes from connecting AI to the systems where work already happens. We build the integration layer with the access controls and audit requirements enterprises expect.
Business problems addressed
- AI pilots isolated from real business systems
- Strict security, data residency, or review requirements
- Legacy systems with limited or inconsistent APIs
Technical capabilities
- REST and webhook integrations with CRM, ERP, and ticketing systems
- Service-account and least-privilege access design
- Data pipelines and vector indexes over existing stores
- Adapters for legacy systems and file-based exchange
- Deployment into your cloud account or private network
Example use cases
- Assistant that reads and updates CRM records with approval
- Search over internal documentation with permission-aware results
- Middleware that normalises data between ERP and external services
AI Agent Monitoring and Optimization
Agents drift as data, models, and requirements change. We instrument them, evaluate them against fixed test sets, and improve them in controlled releases.
Business problems addressed
- No visibility into why an agent produced an answer
- Quality regressions after a model or prompt change
- Rising token cost with no clear attribution
Technical capabilities
- Tracing of model calls, tool calls, and decisions
- Regression suites with automated and human-reviewed evaluations
- Cost, latency, and failure-rate dashboards and alerts
- Prompt and model version management with safe rollout
- Incident review and corrective updates
Example use cases
- Evaluation harness before every agent release
- Review queue for low-confidence outputs
- Cost optimisation through model routing and caching
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