AI Agent Development Company in Manchester

Manchester agent projects fall into three bands, and the gap between them is wide: a scoped Manchester AI agent proof of concept, a production agent in customer operations or internal copilots, or multi-agent platforms or contact centre deployments at Boohoo or AO.com scale. Scope drivers include workflow audit, a single-agent build on OpenAI or Anthropic, an evaluation harness, and a hosted demo. Manchester rates sit below London but reflect the talent depth from dock10, University of Manchester, MMU, and the Booking.com UK and Auto Trader engineering benches. Everything is fixed fee against written scope, with model and infrastructure costs broken out separately.

Every agent build opens with a Data Protection Impact Assessment that classifies personal data by category, identifies lawful basis per processing activity, and maps every tool call to a controller-to-controller or controller-to-processor agreement. We default to UK and EU regions for inference and retrieval, prefer ephemeral logs for prompts containing personal data, and apply ICO-aligned data minimisation by passing IDs and references rather than raw PII wherever the workflow allows. For Subject Access Requests we maintain a queryable audit log keyed to the customer identifier so you can reconstruct every agent decision touching a data subject within the 30-day statutory window. Erasure under Article 17 is handled by purging the audit log and retraining or re-evaluating any model that was fine-tuned on the relevant data.

Yes. For Co-operative Bank, Manchester Building Society, and University of Manchester research workloads where closed-API egress is blocked, we deploy Llama 3.3 70B, Qwen 2.5 72B, or Mistral Large 2 on Azure UK South or AWS eu-west-2 GPU instances behind a VPN gateway. Tool calling works through the model's native function-calling format or via structured JSON prompting with Pydantic validation. Performance on tool use, retrieval grounding, and tier-one workflows is close enough to GPT-4.1 and Claude 4.7 for most internal use cases, and the data residency story is unambiguous. We do not pretend self-hosted models match frontier closed APIs on every benchmark, so we run an evaluation harness during discovery to confirm a self-hosted choice is fit for purpose before locking it into a production design.

Agent tools are typed Python or TypeScript wrappers around the official Salesforce REST and Bulk APIs, Microsoft Graph and Dynamics 365 OData endpoints, and the NetSuite SuiteTalk REST and SOAP layers. Each tool carries an explicit authorisation scope, so an agent that can read a Salesforce case cannot quietly write to a financial transaction without a separate grant that is logged, attributable, and revocable. For high-volume retail clients with Boohoo or AO.com style throughput we add a queue-based execution layer (SQS or Service Bus) with retry, dead-letter, and idempotency keys, so an agent never silently double-processes a refund during a 30-second AI provider hiccup. Every tool call is logged to a UK-region SIEM with input, output, latency, and the model and prompt version that produced it.

Both, and the choice depends on the risk tier of the decision, not on what is technically possible. For high-impact actions (issuing a refund above a threshold, sending a regulated communication, executing a payment, changing a customer's plan) we default to human-in-the-loop with the agent drafting and a human approving inside a familiar surface such as Salesforce, Zendesk, Intercom, or Microsoft Teams. For low-impact actions (categorising a ticket, enriching a product record, drafting a reply for review, updating an internal knowledge base) we let the agent act and log the action. Manchester financial services work almost always sits in the human-in-the-loop tier because FCA Consumer Duty and SYSC controls require it. We document the tiering decision in writing and bring legal and risk into the conversation early.

Every agent has an evaluation harness with at least fifty golden tasks built from real customer transcripts, real internal documents, or simulated edge cases produced with your domain experts. Tasks are scored on tool selection accuracy, factual correctness, citation quality where retrieval is involved, latency, and a custom rubric for tone and policy adherence written specifically for your brand. We use Ragas for retrieval-augmented generation evaluation, DeepEval for general LLM assertions, and LangSmith or Langfuse for trace-level analysis. Pre-production runs the harness on every commit. Post-production we sample real runs, score them weekly against the same rubric, and watch for drift. When drift exceeds a defined threshold we retrain, re-prompt, or roll back to a known-good version with an explicit changelog.

Consumer Duty (PRIN 2A) demands that firms deliver good outcomes for retail customers across products, price and value, consumer understanding, and consumer support. For agents this means we cannot let an LLM hallucinate fees, terms, or eligibility. Our financial services agents pin product and pricing facts to a verified knowledge base with version control, refuse to generate numerical answers outside the retrieved facts, escalate any unclear customer query to a human, and log every interaction with sufficient detail for the firm to evidence good outcomes during a Consumer Duty board review. We coordinate with your appointed Consumer Duty champion and compliance counsel rather than replacing them, and we provide a written control narrative that an FCA Skilled Persons review under section 166 could read without a frantic week of back-fill.

Production support includes 24x7 on-call coverage from our Edmonton bench (afternoon GMT into your evening), Chandigarh bench (overnight UK), and Manchester-aligned overlap from our Edmonton and Chandigarh leads during 9am to 5pm GMT and BST. We monitor model provider availability for OpenAI, Anthropic, and Azure, alert on cost anomalies, and run weekly evaluation sweeps against the golden task set. Quarterly we re-baseline the harness against current production traffic, refresh prompts for any model upgrades, and produce a written report for your governance forum. SLAs are 15 minutes for production-down incidents, 4 hours for high-severity quality regressions, and next business day for low-severity issues. All of this is fixed fee on an annual support contract with no hidden T and M overruns.