Enterprise AI Agents & Autonomous Workflow Pipelines
Replace manual data entry, disconnected app transfers and delayed customer handoffs with custom-built AI agents operating 24/7 with zero latency.
What this is costing you right now
Cross-department systems updated by hand, so every record is stale for most of the working day.
Copy-paste between invoicing, CRM and order tracking introduces errors that surface weeks later in reconciliation.
Headcount scales with transaction volume instead of software absorbing it.
What gets delivered
| Component | Technical Execution | Client Benefit | Output |
|---|---|---|---|
| Custom Agent Development | Retrieval-augmented or fine-tuned agents scoped to one domain task, with evaluation sets per workflow. | Behaviour you can test, not a chatbot you have to trust. | Deployed agent + eval suite |
| Document Processing (OCR + LLM) | Extraction and structured insertion from PDFs, invoices, purchase orders and email bodies into your database. | Paper and email stop being a data entry queue. | Parser service + schema |
| Multi-Step Orchestration | Conditional, retrying workflows on n8n, Make, or Python microservices with idempotent steps. | A failed third-party call retries instead of dropping the record. | Orchestration graph + runbook |
| Human-in-the-Loop Triggers | Exception routing to Slack, WhatsApp or Teams whenever a configurable confidence threshold is not met. | Automation never silently guesses on the edge cases. | HITL queue + escalation policy |
Architecture & Tools
- Python
- LangChain / LlamaIndex
- n8n
- Supabase
- Current-generation frontier models from Anthropic and OpenAI, selected per workload latency requirement
- Speech-to-text
- Vision extraction
- Pinecone
- Qdrant
- pgvector
- Managed cloud
- Local inference via Ollama / vLLM where data cannot leave premises
- shieldConfigured on dedicated enterprise API endpoints with zero-data-retention terms, so client payloads are not used for model training.
- shieldEnd-to-end encryption in transit and at rest; credentials held in a managed secret store, never in code.
Timeline & methodology
Process mapping, system access audit, and a written problem statement signed off before anything is built.
Functional Specification Document (FSD), data model, integration contracts, and acceptance criteria.
Implementation against the FSD on an isolated staging environment, with weekly demos.
Production migration, team handover, documentation, and the start of the defect warranty window.
Measured against a baseline
- Challenge
- 15 hours a week spent extracting freight data from emailed PDFs into legacy ERP software.
- Solution
- Document-parsing agent with automated validation rules and a human review queue for low-confidence extractions.
Who this is for
- Businesses with 10+ staff processing 100+ repetitive entries, tickets or documents a day
- Teams already running two or more systems that do not talk to each other
- Operations where a wrong record costs real money downstream
- Solo operators looking for ChatGPT prompts or an off-the-shelf wrapper
- Teams without a documented process to automate — we would be automating chaos
- Anyone needing it live next week
Final scope is quoted against the Functional Specification Document agreed in Week 2.
Bound guarantees
All code, designs and workflow blueprints transfer to you on final milestone payment. No proprietary lock-in, no licence-back clauses.
Guaranteed on the core production build at handoff, measured before third-party tracking scripts and client-supplied unoptimised assets.
Zero-cost resolution of functional defects that deviate from the approved Functional Specification Document, for 30 days after cutover. Scope changes are quoted separately.
A Non-Disclosure Agreement is signed before any operational data, credential or codebase is shared in either direction.
Send your top three bottlenecks
We will come back with a written feasibility read — what is automatable now, what is not, and what it would cost.