Workflow Automations · for Operations leaders adopting AI
AI-Augmented Back-Office Automations
In short
Aqib Ops builds AI-augmented back-office automations in 2–4 weeks per workflow. We use GPT, Claude, and OCR models to handle invoice extraction, contract review, document triage, and support classification — always with a human-in-the-loop checkpoint when stakes are high.
The problem
Most AI back-office projects either over-automate (hallucinated invoice line items posted to QuickBooks) or under-automate (a chatbot that just opens a support ticket). The pragmatic middle — AI does the boring 80%, humans review the consequential 20% — is rarely shipped.
Our approach
We model the human review checkpoint first, then automate up to it. For OCR-heavy work, we combine cloud OCR (Google Document AI) with LLM normalization. For classification, we eval against a held-out set before going live. For extractions, we always show the source span.
Stack we'd reach for
Google Document AI / Mindee
Best-in-class OCR for invoices, receipts, structured docs.
OpenAI / Anthropic
Normalization, classification, extraction with structured outputs.
Inngest
Durable AI workflows; retries on rate limits; observable runs.
Postgres + pgvector
Storing extractions, audit trails, and similarity-based dedup.
Slack approval bot
Human-in-the-loop checkpoints where stakes are high.
What you'd get
- Invoice OCR → normalized line items → AP review → QuickBooks post
- Contract clause extraction with source-span citations
- Inbound support email classification + draft reply
- Document deduplication via pgvector similarity
- Slack approval bot for stakes-aware human review
- Eval harness with golden dataset for every model task
Recent example
A mid-market construction firm
AP processing time dropped from 6 days to under 24 hours. 92% of invoices now post automatically; the remaining 8% route to a human reviewer with the AI's draft pre-filled.
Frequently asked
When should we use AI vs. a deterministic automation?
Use deterministic when the rules are stable and the inputs are clean (Shopify webhooks). Use AI when inputs are messy (PDFs, free-text emails, contracts) and the human cost of doing it manually exceeds the AI's error rate.
How accurate is the AI on invoice / contract extraction?
On well-structured invoices, modern Document AI + LLM stacks hit 96–99% accuracy on line items. On contracts, clause extraction with citations runs 88–94% — high enough to be useful with human review on flagged cases.
What about hallucinations?
We use structured outputs (JSON schema) and always require a source-span citation for extractions. Hallucinations become detectable: if the model can't cite the source, the human reviews. This pattern eliminates ~95% of hallucination risk in extractive tasks.
How much does an AI back-office automation cost?
Per workflow: $12k–$30k for the build, plus ongoing model + OCR usage (typically $0.05–$0.50 per processed document). Most automations pay back within 4–8 months on labor savings alone.
Will the AI get smarter over time?
Yes — we capture every human correction as a training signal. After 3–6 months, we run a fine-tune or prompt update against your accumulated corrections, which usually drops error rate by another 30–50%.
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