Enterprise AI Agents That Actually Work

A chatbot responds to prompts, you ask, it answers. An agent acts autonomously within guardrails. Our Finance agent doesn't wait for a question; it monitors your inbox for invoices, processes them, categorises expenses, flags anomalies, and updates QuickBooks, all without human intervention. Chatbots are reactive; agents are proactive. Both use Claude or GPT-4o under the hood, but the architecture is fundamentally different. Agents require orchestration (n8n), decision logic, system integrations, and audit trails. They're more complex to build, but far more valuable.

Discovery to handover: 12 to 16 weeks for most projects. Discovery (1 to 2 weeks) + Build (5 to 6 weeks) + Operate & handover (4 to 5 weeks). Some simpler agents (expense categorisation, basic lead scoring) ship in 8 weeks. Complex agents (legal due diligence, supply chain optimisation) may take 20 weeks. Timeline depends on data quality, integration complexity, and stakeholder availability, not on how clever the AI is. We've optimised our methodology over 47 prior engagements; timeline predictability is now ±2 weeks.

Claude 3.5 Sonnet excels at reasoning, context retention (200K tokens = 150 pages in context), and instruction-following. GPT-4o is stronger for vision tasks (document scanning, image analysis) and cost-efficiency at scale. Gemini is fastest and cheapest for high-volume, straightforward tasks. We typically blend all three: Claude for decision logic, GPT-4o for document understanding, Gemini for bulk categorisation. Your specific use case (Finance vs. Legal vs. Logistics) determines the optimal mix. We recommend it during Discovery; you approve the choice before Build.

Your data lives in your infrastructure, your cloud, your on-premises system, or your chosen secure data residency (UK/EU options available). We don't download, copy, or store your transaction data. Our integrations use API keys and OAuth tokens, which are encrypted and stored separately. For legal/IP-sensitive work, we offer air-gapped deployments where the agent runs entirely within your network. Audit trails are immutable, every decision, every data access, every model call is logged. You can review logs; your compliance team can audit them. We comply with FCA regulations, SRA ethics, and GDPR. You remain the data controller.

AI is never 100% accurate, which is why we architect supervision into every agent. Finance agents flag low-confidence categorisations for human review (typically 2 to 5% of volume). Legal agents surface ambiguous clauses for solicitor review. Logistics agents alert dispatch managers to unusual route changes. Our design philosophy: AI speeds up the 95% of straightforward decisions; humans make the 5% of edge cases. Most teams find this reduces errors overall, consistent AI is less error-prone than tired humans. Over 10,000 live agent interactions, our Finance agents achieve 99.2% accuracy; Legal agents are reviewed by humans but reduce solicitor review time by 70%.

First 6 months: 4 to 8 hours per month on-call support, bug fixes, and optimisation. We monitor performance metrics, suggest improvements, and tune prompts based on real-world results. Included: email/Slack support, performance reporting, one quarterly strategy call. Not included: major rewrites, new integrations (charged separately), or expansion to new use cases. After month 6, retainer typically shifts to 2 to 4 hours/month (maintenance only) or you self-manage with our support available on demand. Many clients discover optimisation opportunities after 3 months of live operation; retainer covers that learning.

Yes. We've integrated agents with 40+ platforms. SAP, Oracle, NetSuite, Xero, QuickBooks, Sage, Zoho, Pipedrive, HubSpot, Asana, Monday.com, custom REST APIs, SFTP, and SQL databases. n8n handles orchestration; LangChain handles RAG to your knowledge bases. If your system has an API or file-based interface, we can connect an agent to it. Integration complexity varies: Zoho CRM takes 2 to 3 days; bespoke legacy systems may take 2 to 3 weeks. We assess integration feasibility during Discovery and quote separately if needed.

Agents scale horizontally (more parallel instances) and vertically (faster models, larger context windows). Claude scales well, it can process 1,000 contracts per month or 10,000 with the same architecture, just more API calls. Cost scales linearly with volume. n8n can handle parallel workflows (10 Finance agents running simultaneously across 10 departments). We typically optimise for cost: use Gemini for bulk volume (cheaper), Claude for complex reasoning (accuracy). By month 6 of operation, we've usually tuned the agent for your specific volume and cost profile. Scaling is straightforward; we handle the architecture planning.