Discovery to POC Discovery & design to a tested, documented agent prototype
Auditable Actions Every agent action that touches a system of record is logged
Unguarded Writes HITL gates, permission scoping & output validation on every workflow
Security Standard SOC 2, GDPR, HIPAA — compliance designed in, not bolted on
A production AI agent maintains context across steps, selects and calls tools, handles partial failures, and produces verifiable outputs — all within boundaries your organisation has defined. We build on the OpenAI Agents SDK for native tool-calling and handoff primitives, and on Claude’s tool use capability for reasoning-intensive multi-step tasks.
Multi-agent systems fail in predictable ways: state lost between steps, tool call timeouts with no recovery path, parallel agents producing conflicting outputs. We use LangGraph for stateful graph-based execution, and Temporal or Apache Airflow for long-running durable workflows that survive infrastructure restarts.
An agent that cannot read from and write to your actual systems of record cannot do real work. We implement function calling and tool use natively, giving agents structured, typed access to REST APIs, databases, document stores, and enterprise applications. MCP makes your tool layer portable and model-agnostic.
A model’s training data ends at a cutoff and contains none of your proprietary contracts, compliance policies, or client records. RAG bridges that gap by retrieving the specific documents, records, or data fragments relevant to the current task. We implement hybrid retrieval and cross-encoder re-rankers for enterprise precision.
The case for agentic AI is not that humans leave the workflow — it is that humans engage at the right moments. HITL checkpoints are designed at the architecture stage, not added as safety patches. Guardrails operate at multiple layers: permission scoping, PII controls, policy enforcement, and fallback routing.
Agentic systems fail in ways that are opaque by default. We instrument every production deployment with LangSmith or Langfuse — capturing full execution traces: every LLM call, every tool invocation, and every agent decision point. Evaluation is built into the deployment pipeline, not bolted on post-launch.
A production AI agent maintains context across steps, selects and calls tools, handles partial failures, and produces verifiable outputs — all within boundaries your organisation has defined. We build on the OpenAI Agents SDK for native tool-calling and handoff primitives, and on Claude’s tool use capability for reasoning-intensive multi-step tasks.
Multi-agent systems fail in predictable ways: state lost between steps, tool call timeouts with no recovery path, parallel agents producing conflicting outputs. We use LangGraph for stateful graph-based execution, and Temporal or Apache Airflow for long-running durable workflows that survive infrastructure restarts.
An agent that cannot read from and write to your actual systems of record cannot do real work. We implement function calling and tool use natively, giving agents structured, typed access to REST APIs, databases, document stores, and enterprise applications. MCP makes your tool layer portable and model-agnostic.
A model’s training data ends at a cutoff and contains none of your proprietary contracts, compliance policies, or client records. RAG bridges that gap by retrieving the specific documents, records, or data fragments relevant to the current task. We implement hybrid retrieval and cross-encoder re-rankers for enterprise precision.
The case for agentic AI is not that humans leave the workflow — it is that humans engage at the right moments. HITL checkpoints are designed at the architecture stage, not added as safety patches. Guardrails operate at multiple layers: permission scoping, PII controls, policy enforcement, and fallback routing.
Agentic systems fail in ways that are opaque by default. We instrument every production deployment with LangSmith or Langfuse — capturing full execution traces: every LLM call, every tool invocation, and every agent decision point. Evaluation is built into the deployment pipeline, not bolted on post-launch.
Production experience across accounts payable, claims triage, logistics exception management, compliance Q&A, and sales automation — each with defined success metrics and measurable business outcomes.
Deep, hands-on experience with LangGraph, AutoGen, CrewAI, Temporal, and Apache Airflow. We select the right orchestration stack for your workflow's durability and complexity requirements — not the one we know best.
Azure OpenAI, Amazon Bedrock, Google Vertex AI, or fully on-premises with vLLM-served open-weight models. Agent infrastructure deployed within your network boundary for data residency-sensitive environments.
Continuous AgentOps post-deployment: trace analysis, eval re-runs, cost and latency trending, and periodic guardrail reviews. We flag degradation before it becomes a business problem and iterate agent design as your requirements evolve.
A tested, documented agent prototype as the POC deliverable. Signed-off evaluation report before production deployment. Runbook covering architecture, integration points, and common failure mode responses at handover.
ISO 27001, SOC 2 Type II, GDPR, HIPAA — compliance is a design constraint from the first architecture decision. MCP-based tool layers, agentic RAG, GraphRAG, and multimodal agents are part of our active development practice.
LangGraph
AutoGen
CrewAI
Temporal
Apache Airflow
OpenAI Agents SDK
Claude Tool Use
MCP (Model Context Protocol)
LangChain
LlamaIndex
Pinecone
Weaviate
pgvector
Azure AI Search
LangSmith
Langfuse
Ragas
Azure OpenAI Service
Amazon Bedrock
Google Vertex AI
vLLM / TGI
Agents and self-service bots retrieve the exact product documentation and past resolution notes relevant to each issue — generating a cited, structured response in under two seconds. Escalation rates fall. Handle time falls.
HR teams deploy a RAG assistant over policy documents and HR handbooks. IT helpdesks give staff instant access to setup guides, VPN instructions, and access-request procedures — always drawn from the current document version.
Sales reps ask in natural language for competitive comparisons, product capabilities, or pricing policy details during a live call. The RAG assistant retrieves from approved documentation — not from model memory — ensuring responses are consistent with current positioning.
Legal teams query large contract archives to surface obligation clauses, renewal dates, liability caps, and governing law provisions across hundreds of agreements simultaneously — with a direct link to the source document and page number.
Which accounts in the North region have had no contact in the last 60 days and have a renewal due this quarter?" The RAG system translates this into a live CRM query, returns a ranked list, and explains the query logic. No SQL skills required.
Compliance officers query regulatory document libraries — FCA, SEC, PRA policy updates, internal policies — with permission-aware retrieval that ensures each user accesses only their authorised document set and every query is logged for audit.
Replaces a static retrieval step with an agent that plans its retrieval strategy. For multi-part queries — "Compare our EMEA pricing policy from 2023 with the current version and flag any changes that affect enterprise tiers" — the agent issues multiple targeted retrievals, synthesises across result sets, and constructs a structured answer. Built on LangChain and LlamaIndex agent interfaces with explicit state management for multi-step retrieval chains.
Represents the knowledge base as a graph of entities and relationships rather than a flat chunk store. When questions require understanding how entities relate to each other — organisational structures, supply chain dependencies, regulatory cross-references — graph traversal retrieves more contextually complete information than vector similarity can. Particularly effective for legal, compliance, and enterprise knowledge management use cases.
Extends retrieval beyond text to images, diagrams, and tables embedded in documents. Technical manuals, financial reports, and product catalogues contain critical information in non-text formats. We build ingestion pipelines that extract and index these elements, and retrieval pipelines that return them as grounding context alongside text chunks — enabling answers that correctly reference figures, charts, and structured data.
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An expert contacts you after having analyzed your requirements;
If needed, we sign an NDA to ensure the highest privacy level;
We submit a comprehensive project proposal with estimates, timelines, CVs, etc.
A chatbot or copilot responds to a query it produces text that a human then acts on. An AI
agent acts directly: it reads from your systems, calls tools, executes workflow steps, and
writes outputs to systems of record. The distinction is consequential for back-office
automation, where the value is in removing manual steps, not in producing better text for a
human to process manually.
Agentic AI creates the most value in workflows that are multi-step, rule-governed, data-intensive, and currently dependent on manual data movement between systems. Accounts payable, claims processing, supplier onboarding, and compliance reporting are strong candidates. Workflows that require creative judgement, political context, or novelproblem-solving as the primary activity are not well-suited for autonomous agent execution though agents can assist with information gathering and preparation in those workflows.
Through HITL gates, permission scoping, and output validation not through model confidence. Agents are designed with explicit boundaries: actions above a defined consequence threshold require human approval before execution. Tool access is provisioned at the minimum required scope. Every output that enters a system of record passes through a validation step. Mistakes happen at the edges; the system is designed so that edge cases route to humans rather than proceeding autonomously.
Yes. We build connectors to SAP, Oracle, Salesforce, ServiceNow, and custom internal APIs as a standard part of the Tool & System Integration work. Where your systems expose a REST API or support OAuth, we can integrate. For legacy systems without APIs, we work with your engineering team to identify viable integration points RPA, database-level connectors, or structured file exchange and are transparent about the trade-offs each approach involves.
A Discovery and POC engagement typically runs six to ten weeks: two to three weeks of Discovery and Agent Design, followed by four to six weeks of POC build and evaluation. The POC targets one scoped workflow enough to validate agent behaviour on real data and demonstrate system integration. Production deployment timelines depend on the complexity of the workflow and the maturity of your integration infrastructure.
Development and testing use anonymised or synthetic data wherever possible. Where real data is required to achieve meaningful evaluation common in RAG pipelines where domain-specific retrieval behaviour cannot be replicated with synthetic data all handling is governed by a signed data processing agreement. Production data is not retained beyond the evaluation period and is not used for model training or fine-tuning.
AgentOps. We instrument every production deployment with observability tooling, baseline performance metrics at launch, and maintain a monitoring and evaluation programme post-deployment. Agent performance degrades when underlying data distributions shift, model versions change, or upstream system outputs change format. We catch those degradations in monitoring before they surface as business problems, and we iterate the agent design in response.
Your team already knows which workflows are too slow, too error-prone, and too dependent on manual data movement. We scope a pilot in a 90-minute discovery session no commitment required, no vendor presentation. You leave with a concrete workflow brief and an architecture recommendation.
