Services / AI Engineering
We build agents that use your knowledge, connect to your systems and carry out defined work. With clear permissions, measurable quality and people in control of consequential decisions.
Custom development. Your existing systems. A bounded place to start.
The business case
A useful agent has a defined outcome: a request resolved, a document checked or a handoff completed. We identify where language, messy inputs or changing context make AI useful, then build the software and controls around it. If a script, search interface or rules engine is the better answer, we say so.
No reliable source of truth. Unclear permission to use the data. A high-impact decision with no accountable reviewer. Or a task where an error cannot be detected or safely contained.
We address those gaps before increasing autonomy. More tool access is not automatically more business value.
What we can build
These are example engagements, not claims of deployed customer results. Scope depends on your systems, data quality, policies and integration access.
Retrieve permitted account context, investigate a support request and draft a response or propose a bounded action.
Refund exceptions, sensitive complaints and commitments go to your team.
Measure: Resolution time, escalation quality and rework.
Research accounts from approved sources, qualify enquiries and prepare CRM updates, meeting briefs or proposal drafts.
People approve pricing, outreach and contractual terms.
Measure: Preparation time, source accuracy and useful handoffs.
Extract information from invoices, forms or contracts, check it against business records and route discrepancies for review.
Missing evidence and conflicting records are flagged, not guessed away.
Measure: Field accuracy, exception rate and processing cost.
Find answers across authorised documents, cite the evidence and prepare IT, HR or operations requests through scoped tools.
Source permissions follow the user; access changes require authorisation.
Measure: Grounded answer quality and successful request completion.
Match invoices to orders, compare supplier information and prepare reconciliation or purchase recommendations.
Payments, supplier selection and spend exceptions remain approved decisions.
Measure: Matching accuracy, review effort and duplicate prevention.
Add a task-oriented assistant to your SaaS product, with tenant-aware context, validated actions and a visible handover to a person.
Every action is constrained by the signed-in user's permissions.
Measure: Task completion, adoption and cost per successful outcome.
System design
A production agent needs trustworthy context, narrow tools and an execution path that can be inspected and interrupted. We keep the simplest architecture that meets the task; multi-agent coordination is added only when it earns its complexity.
Connect approved sources, preserve document access controls, and retrieve relevant evidence with citations. Define freshness, deletion and fallback behaviour. A vector database is optional, not the starting requirement.
Use a model for language understanding and bounded decisions. Use deterministic code for calculations, policy rules and known sequences. Add persistent state when work must pause, resume or survive an outage.
Expose narrow, typed actions over your APIs. Validate arguments and authorise each call outside the model. MCP can standardise a tool interface; it does not replace authentication or security review.
Separate proposing an action from committing it. Show reviewers the evidence and intended change. Use idempotency keys, transaction boundaries and bounded retries where the connected system supports them.
Link requests, retrieved evidence, model versions, tool calls and approvals in a run trace. Redact sensitive fields, limit retention and monitor failures, latency and spend.
Integrate, don't replace
We connect agents to the records and actions your workflow needs: CRM, service desks, ERP, document stores, communication tools and internal APIs. Existing systems remain the source of truth.
Validate access and source quality with a read-only integration first. Write actions get explicit schemas, permission checks and test environments where available.
Rate limits, stale records, missing webhooks and legacy interfaces affect the design. We document constraints and retain a human step when safe automation is not feasible.
Keep tenant and user permissions through retrieval and tool execution. The agent cannot inherit administrator access just because it needs to complete a task.
Safety & human control
Prompts can guide behaviour; they are not an access-control system. We enforce restrictions in tools, identity, application logic and infrastructure, with controls proportional to the workflow's risk.
Allow only required actions and destinations. Keep credentials outside model context and validate every request at the execution boundary.
Treat retrieved documents, email and tool responses as data, not instructions. Test prompt injection and unsafe action attempts; no single filter eliminates the risk.
External communications, material record changes and financial actions can require explicit review. Silence, a timeout or the agent's own recommendation is not approval.
Agree which information can enter a model, what is redacted and how long evidence is retained. Review provider terms, residency needs and access to traces.
Use bounded retries, checkpoints and duplicate protection where possible. Provide a pause, revoke or handover path when a dependency fails or the task leaves scope.
Name a business owner and an operational contact. Document who reviews incidents and authorises changes. Compliance requires your legal and security team's assessment.
Evaluation & production readiness
A convincing demo is not a release criterion. We build an evaluation set from representative tasks and known exceptions, agree acceptance thresholds with you and repeat the checks when models, prompts or tools change.
Completion, extraction accuracy, grounded answers and whether the final result actually meets the user's request.
Correct arguments, authorised actions, refusal of prohibited requests and escalation when evidence is missing.
Timeout recovery, duplicate prevention, durable handoffs and behaviour during provider or integration outages.
End-to-end response time, model and retrieval spend, tool usage and reviewer effort per successful task.
Model-based scoring can help scale evaluation, but it also makes mistakes. We calibrate it against human-reviewed examples, keep a separate holdout set where practical and do not use a model's self-reported confidence as the only safety check.
Technology stack
These are relevant options we can evaluate for an engagement, not a claim that every project uses them all. We choose around your requirements, existing platform and your team's ability to maintain the system. Tool choice is confirmed during architecture design.
Stateful graphs, tool handoffs or typed Python agents, selected around the workflow. We usually choose one primary orchestration approach rather than combining frameworks without a reason.
Managed model APIs or open-weight inference where feasible. Selection depends on evaluation results, modality, region availability, data terms, latency and total operating cost.
Document ingestion, metadata filtering, hybrid retrieval and reranking when needed. Search and structured queries may be more suitable than retrieval-augmented generation for some tasks.
Typed APIs, product interfaces and controlled connections to your systems. Tool schemas, identity checks and integration tests matter more than the transport protocol.
Long-running workflows, checkpoints, queues and application records where required. Durable orchestration complements agent logic; it does not make model outputs correct.
Tracing, versioned evaluation sets, adversarial checks and conventional software tests. Human-reviewed outcomes calibrate automated scoring before it becomes a release gate.
Use your existing platform when possible. Add managed secrets, scoped service identities, CI/CD and infrastructure controls. Kubernetes is an option for operational needs, not a prerequisite.
Frameworks change quickly. We check licensing, maturity, security posture and provider data terms before committing. Model portability is designed where it is useful, but switching providers still requires regression testing.
Delivery approach
We work with your business owner and technical team, connecting implementation decisions to an agreed outcome. Each phase produces something reviewable, with a clear decision about what happens next.
Work alongside your process owner to map the current steps, volumes, exceptions and baseline. Check whether simpler automation would solve the problem first.
Output: Workflow map, feasibility risks and measurable acceptance criteria.
Agree permitted data, tools, human checkpoints and failure behaviour. Validate API access and provider terms before promising an integration or deployment model.
Output: Architecture, tool contracts, permission model and evaluation plan.
Implement one useful end-to-end path using representative, authorised data. Include the reviewer interface and failure handling, not just the model interaction.
Output: Working pilot, integration tests and a versioned evaluation set.
Compare against your baseline, test adversarial inputs and observe exceptions. Use shadow mode or read-only operation before enabling consequential actions.
Output: Evaluation report, unresolved risks and a go/no-go review.
Roll out to a limited group with monitoring, ownership and rollback procedures. Expand scope only after agreed quality and operational checks are met.
Output: Deployment, runbooks, handover and an agreed support arrangement.
Handover & ongoing operation
Deliverables are agreed to scope, not assumed. The aim is a maintainable capability your team can inspect, operate and change, rather than an opaque demo dependent on one developer.
Source code, integration contracts, deployment configuration and agreed repository access. Ownership and third-party licence terms are documented in the engagement.
Versioned evaluation cases, release checks, trace configuration and known limitations. A record of what was tested and which risks remain.
Reviewer guidance, operator training, incident and rollback procedures. Clear responsibility for infrastructure, approvals and ongoing support.
Economics & engagement
Compare the full workflow cost with your current process, including model calls, retrieval, integrations, infrastructure, review time and ongoing maintenance. Tokens saved are not a business outcome if the task still needs to be done again.
Start with a paid two-week sprint to assess a workflow and build the agreed slice. We define deliverables, access requirements and review points before starting. No long-term commitment is required; a production launch may need further work.
A process owner, representative cases, authorised data access and someone who can unblock integrations. We will also agree who reviews security requirements and what success would look like against your current baseline.
Engineering blueprints
Practical guides to the controls and evaluation practices behind dependable agent workflows.
Questions before you build
A chatbot primarily responds to messages. An agent can use tools and workflow state to carry out a bounded task. Many useful products combine both. The important question is what work it can complete safely, not which label it uses.
Not necessarily. One agent with narrow tools or a deterministic workflow is often easier to evaluate and operate. Multiple agents make sense when specialist responsibilities or independent checks justify the extra handoffs, latency and complexity.
Yes. We can review its architecture, integrations and evaluation coverage, identify the production gaps and agree whether to improve it or rebuild selected parts. We do not assume a working demo is ready for unrestricted live use.
We can assess private-cloud and self-hosted options. Feasibility depends on model capabilities, hardware, provider terms and your integrations. A private application does not mean data stays private if it still calls an external model API.
Data readiness, integration access, workflow complexity and security requirements drive the scope. We can start with a paid two-week discovery and build sprint, agreeing its deliverables in advance. Production timelines and pricing follow the assessment; the sprint is not a guarantee of a complete production system.
Ownership, repository access, third-party licences and operating responsibilities are set out in the engagement agreement. We plan a documented handover or an ongoing support arrangement, including who handles alerts, approvals and model changes.
No model is error-free, and a framework does not confer compliance. We agree task-specific quality thresholds, escalation paths and controls, then test them with representative cases. Your business, legal and security owners remain involved in consequential decisions.
Build something useful
Tell us what your team is trying to get done, where the process breaks and which systems it touches. We'll work through feasibility, boundaries and a sensible first scope.