Document and data extraction
AI extraction from PDFs and free text into strict structured data with per-field confidence, and a review queue for anything missing or conflicting.
Methodspire
AI AGENTS & WORKFLOW AUTOMATION
AI agents and automation you can trust with real work.
AI is useful when it is dependable. I build agents and automations with validation, fallbacks, confidence scores, audit trails and human review wherever a mistake would be costly.
Who this is forOperations leaders and founders who want AI to handle documents, customer conversations and routine workflows without losing control or visibility.
What you get
Agents with knowledge and hand-off
RAG-based assistants over your own content, tenant-aware, with a clean hand-off to a human.
CRM and messaging automation
Sync with HubSpot, GoHighLevel and Zoho, WhatsApp Business API with approved templates, and omnichannel inboxes.
Safe by design
Provider fallback, prompt-injection guardrails, read-only database access for text-to-SQL, idempotent actions and an audit log of every AI call.
How we work
- Pick the right first workflowWe choose a narrow, measurable workflow where automation is dependable.
- Build with guardrailsValidation, confidence thresholds, fallbacks and human review are part of the first version.
- Measure and extendLogs and evaluation show what works, then we widen the scope deliberately.
Relevant experience
- Document and CRM automation with AI: extraction, review queue, deduplication and sync to several CRMs.
- Document intake from Gmail, WhatsApp and SMS with OCR, classification and routing to cloud storage.
- On-premise, air-gapped AI decision support with a local language model, citations and a full audit log.
Client names are withheld; projects are described by the system built.
Technologies
- OpenAI
- Anthropic
- LangChain / LlamaIndex
- pgvector / Qdrant / Pinecone
- Python / FastAPI
- n8n
- Make
- WhatsApp Business API
- HubSpot / GoHighLevel / Zoho
Common questions
Can the AI run entirely on our own infrastructure?
Yes. I have built on-premise, air-gapped systems using local models, structured output, citations and human approval.
How do you control AI mistakes?
Confidence scores, validation rules, review queues for uncertain cases, provider fallback and an audit trail for every AI call.