We're not an AI consultancy and we don't hand over slide decks. We're a product engineering team that builds agentic AI capabilities—autonomous workflows, LLM-powered features, multi-agent orchestration—directly into your software, the same way we build everything else: scoped honestly, integrated properly, supported after launch.
Agentic AI goes beyond a chatbot that answers questions. It's software that perceives context, plans a course of action, calls the right tools, and completes a task—with appropriate human oversight at every step that warrants it. We build that into your product.
Multi-step autonomous workflows where an AI agent plans, executes, observes, and iterates—connected to your real systems, APIs, and databases rather than operating in a sandbox.
GenAI capabilities integrated into your product's UI and workflows—not bolted on as a disconnected chat window, but wired into the screens and flows your users already work in.
Conversational interfaces for internal tools, support, or in-product copilots that are grounded in your data and workflows—not generic FAQ bots with no access to your actual systems.
Retrieval-augmented generation that grounds LLM outputs in your documents, product data, and knowledge base—so answers are accurate and current, not hallucinated from general training.
Systems where multiple specialised agents collaborate—one plans, one retrieves, one executes, one reviews—to handle complex tasks that no single model call can reliably complete alone.
Turning manual, rule-bound processes into AI-driven workflows—document processing, decision support, data extraction, and task routing built on top of your existing systems and logic.
Most AI engagements end with a model file, a notebook, or a demo that your team has to maintain without documentation. We treat agentic AI features the same way we treat the rest of the codebase: properly built, integrated, tested, and supported.
Agentic features live in your codebase, follow your deployment pipeline, and are maintained by the same team. No separate AI microservice floating next to your app that nobody owns.
Every agentic feature ships with explicit boundaries: what the agent can and cannot do, what triggers a human review, and what happens when confidence is low—agreed before anything touches real users.
We don't have a preferred vendor to push. We pick the model—Claude, GPT, Gemini, or an open-weight model—based on what your latency, cost, and privacy constraints actually require.
These aren't AI for its own sake. Each use case is one where autonomous reasoning—rather than a lookup or a fixed rule—makes the product meaningfully better.
Agents that read contracts, extract structured data, flag risks, and route documents for review—handling volumes that would take a team weeks, in minutes.
Support agents that look up account data, check order status, trigger actions in backend systems, and resolve issues end-to-end—escalating to a human only when genuinely needed.
Agents that gather information from multiple sources, synthesise findings, and produce structured outputs—accelerating analyst, compliance, and due diligence workflows significantly.
Agentic underwriting assistants that pull credit data, analyse financial statements, and produce a structured risk summary—with the final decision always remaining with a human reviewer.
Explore Finance Solutions →Intake triage, prior authorisation assistance, and clinical documentation support—grounded strictly in approved content, with human review at every clinical decision point.
Explore Healthcare Solutions →AI-driven product recommendations, inventory reorder agents, and customer query resolution systems that plug into your existing storefront and operations data.
Explore ECommerce Solutions →We review your data, systems, and the problem you want to solve—and tell you honestly whether an agentic approach is the right fit, or whether a simpler rule-based flow would do the job better and cost less.
Assessing what data the agent can access, what APIs it needs to call, and what guardrails are required before a single prompt reaches production. Most agentic projects stall here if skipped.
A scoped, working prototype of the agent workflow against real or representative data—so you can evaluate actual task completion quality before committing to a full production build.
Production-grade implementation with proper tool definitions, memory management, error handling, rate limiting, and cost controls—wired into your existing application the same way all our work is.
Staged rollout with explicit checkpoints: what the agent can decide autonomously, what requires human approval, and what triggers a graceful fallback—defined before any real user or business data is affected.
Ongoing task success rate monitoring, prompt iteration, cost tracking, and agent capability expansion as your workflows evolve—run as part of an Application Maintenance engagement.
We pick the model, orchestration framework, and infrastructure that fit your latency, cost, and data-residency constraints—not the stack we happen to know best.
A fixed-scope, time-boxed build to validate an agentic workflow against real data before committing to a full integration. The right starting point when the feasibility isn't yet clear.
Embedded AI engineers working as an extension of your product team on a sprint cadence—for teams building a larger agentic roadmap across multiple workflows or products.
Post-launch monitoring, prompt tuning, agent expansion, and cost optimisation under a retainer—the same model we use for Application Maintenance, applied to agentic features.
See Application Maintenance Plans →A lot of AI engagements end with a Jupyter notebook or a demo your engineering team has to reverse-engineer. We build agentic features as part of the same codebase, the same sprint cycle, and the same team that ships and maintains the rest of your application.
Start with a scoped discovery call. We'll tell you honestly whether agentic AI makes sense for your use case, what it would take to build, and what it won't do.
