CAPABILITY // SRV-01

AI Automation & Autonomous Agents

Stop paying humans to do what software can handle at 10× the speed and zero fatigue.

Most businesses are sitting on hundreds of hours of recoverable time — buried in email triage, manual data entry, repetitive reporting, and copy-paste workflows between tools that don't talk to each other. We build AI agents that eliminate that waste permanently. Not scripts. Not simple automations. Intelligent systems that read context, make decisions, handle exceptions, and hand off to a human only when it genuinely matters.

98% task accuracy · 3× faster operations
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WHAT'S INCLUDED
✓Custom LangChain and LlamaIndex agent loops tailored to your exact workflow
✓Retrieval-Augmented Generation (RAG) over internal wikis, PDFs, and knowledge bases
✓API integrations with Zapier, Make.com, Slack, Notion, HubSpot, Salesforce, and more
✓Automated document, email, and PDF analysis and extraction engines
✓Multi-agent orchestration for complex, branching, parallel workflows
✓Human-in-the-loop checkpoints with audit trails for high-stakes decisions
✓LLM-powered classification, routing, and prioritisation systems
✓Real-time monitoring dashboards with error alerting and replay tooling
WHO THIS IS FOR

Built for teams that need results, not experiments.

Operations Teams
Drowning in manual processes — vendor onboarding, invoice matching, compliance checks — that happen the same way every single week.
SaaS Founders
Wanting to ship an AI-powered feature — document Q&A, automated support, intelligent search — without hiring a full ML team.
Finance & Legal Teams
Needing to extract, validate, and route data from contracts, invoices, or regulatory filings at scale without errors.
Customer Support Leaders
Looking to resolve 60–80% of tier-1 tickets automatically while escalating the rest to the right human with full context attached.
HOW IT WORKS

From first call to production in clear steps.

01
Workflow Discovery
We spend two to three sessions mapping your exact current process — what triggers it, what decisions get made, where humans touch it, what tools are involved. We record the edge cases and failure modes that a naive automation would miss.
02
Architecture Design
We design the agent graph: which LLM handles which step, what tools it calls, where memory is stored, how it handles ambiguity, and what triggers a human review. You see and approve this before we write a line of code.
03
Build & Integration
We build the agent, connect it to your existing tools via API or webhook, and run it against real historical data. We test failure paths, edge cases, and high-load scenarios before you see it.
04
Staging & Parallel Run
The agent runs alongside your existing process for one to two weeks. We compare outputs side-by-side, measure accuracy, and tune prompts and logic until results match or beat human performance.
05
Production Deployment
We deploy to your infrastructure (or ours), wire up alerting, set up the monitoring dashboard, and hand over documentation. We remain on call for the first 30 days and offer ongoing retainer support.
IN DEPTH

The details that separate good from great.

Why custom agents outperform off-the-shelf automation tools

Tools like Zapier and Make.com are excellent for simple, linear trigger-action workflows. But the moment a workflow requires reading a document and understanding its intent, making a conditional decision based on multiple data sources, or recovering gracefully from an unexpected input, these tools hit a wall. Custom AI agents built with frameworks like LangChain allow the system to reason about context, call tools dynamically, and handle the messy reality of real business data — partial inputs, ambiguous formatting, multi-language content, and exception handling — without hard-coded rules that break when the world changes.

RAG: making your internal knowledge usable

Retrieval-Augmented Generation (RAG) is the architecture that lets an AI agent answer questions from your internal data without hallucinating. We index your documents — Notion pages, Confluence wikis, PDFs, Google Drive folders, Slack history — into a vector database (Pinecone or pgvector), and the agent retrieves the most relevant chunks before generating a response. This means your agents stay grounded in your actual policies, product documentation, and historical records rather than guessing from general training data. For support agents, this is the difference between a bot that confidently gives wrong answers and one that pulls the correct refund policy from your handbook.

Security, compliance, and auditability

Enterprise clients need more than automation — they need proof. Every agent we build includes a structured audit log: every input, every decision step, every tool call, every output. Logs are tamper-evident, time-stamped, and queryable. We scope credentials with least-privilege access, store secrets in managed vaults (AWS Secrets Manager, HashiCorp Vault), and never train on your proprietary data unless explicitly contracted. For regulated industries — finance, healthcare, legal — we design agents that flag uncertain outputs for human review rather than proceeding, giving you the efficiency of automation without the compliance risk.

FAQ

Questions we get asked before every project.

How long does it take to build an AI agent?
A focused single-workflow agent — for example, an email triage bot or a document extraction pipeline — typically takes 2 to 4 weeks from discovery to production deployment. Complex multi-agent systems that coordinate across many tools, handle many workflow branches, and require extensive testing against historical data typically take 6 to 12 weeks. We give you a precise timeline after the discovery session, not before, because estimates without understanding your data and integrations are guesses.
Do we need to give you access to our internal systems?
We work with whatever access level you are comfortable with. For many integrations, read-only API keys are sufficient. For others, we use sandboxed staging environments that mirror production but contain no real customer data. For highly sensitive environments — banks, law firms, healthcare providers — we can work with anonymised or synthetic data during development and deploy entirely within your own private cloud, with no data leaving your perimeter.
What happens when the agent makes a mistake?
Mistakes are expected in early stages and planned for. Every agent we build includes a confidence threshold system: outputs below a set confidence level are automatically routed to a human review queue rather than proceeding. All decisions are logged with full context so a human reviewer can see exactly what the agent saw, what it decided, and why. After human correction, we retrain or tune the relevant prompt to reduce recurrence. Over time, the human-review queue shrinks as the agent improves.
Can an AI agent work with our existing tools without replacing them?
Yes — and this is almost always the right approach. We build agents that sit on top of your existing stack and connect via APIs, webhooks, or direct database reads. Your team keeps using Slack, HubSpot, Jira, or whatever tools they depend on. The agent operates in the background, augmenting what already exists rather than requiring a platform migration.
How do you handle LLM hallucination in production systems?
We treat hallucination as an engineering problem, not a fundamental limitation. Techniques we use depending on the task include: constrained output schemas (forcing the model to respond in a structured format it cannot deviate from), RAG grounding (ensuring the model only uses retrieved facts), tool-use verification (the model must call a verification tool before asserting a fact), and multi-agent consensus (two agents independently answer and a third reconciles disagreements). For critical decisions, we always include a human fallback.
What is the typical cost of building a custom AI agent?
Scope varies widely. A focused single-workflow automation typically costs between $3,000 and $8,000. A multi-agent system with several integrations, a custom knowledge base, and an admin dashboard typically falls between $12,000 and $35,000. Ongoing API costs (OpenAI, Anthropic, etc.) are typically $50–$500/month depending on volume and are billed separately. We scope every project in writing before work begins — no surprise invoices.
Do you provide training so our team can manage the agent after handoff?
Yes. Every project includes a handoff session covering how to update prompts, how to read the monitoring dashboard, how to manage the human-review queue, and how to add new document types or workflow branches. We also provide written documentation. For clients on retainer, we handle ongoing tuning and maintenance ourselves.
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