Document extractionInvoice & Document Extraction
Photos and PDFs of invoices, POs and delivery notes become clean rows in your accounting system. Nobody retypes anything.
Ingest → Extract → Review and post
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Prompt Engineering & LLM Application Development
Production-grade LLM features built into the product you already ship — and hardened enough that you can support them.
- Time to value
- Typically 3–8 weeks depending on surface area.
- Deployment
- Hybrid — sensitive steps can stay on-prem
- Category
- AI & Intelligent Automation
The manual version of thisInvictt AI layer
Scope
Structure
Evaluate
Harden
Instrument
Once it's AI-assisted01/The problem
The demo worked beautifully. Then it met real users, real edge cases, a real latency budget and a real invoice, and the distance between a convincing prototype and a feature you can put your name on turned out to be most of the work.
02/How it works
The operating model
What enters, what the system does with it, and what reaches you.
System
Scope
Narrow the feature to something with a checkable right answer. Vague features fail in ways you cannot debug.
System
Structure
Structured outputs with schema validation, so downstream code receives typed data rather than prose it has to parse hopefully.
Check
Evaluate
A regression suite built from real inputs, run on every change. Without it, prompt work is superstition and every deploy is a coin flip.
Check
Harden
Timeouts, retries, fallbacks to a smaller model, graceful degradation, and prompt-injection defences on anything that reads untrusted input.
Check
Instrument
Latency, token spend and quality tracked per feature, so cost is a number you watch rather than a surprise at month end.
Read the detail behind each stage
01
Scope
Narrow the feature to something with a checkable right answer. Vague features fail in ways you cannot debug.
02
Structure
Structured outputs with schema validation, so downstream code receives typed data rather than prose it has to parse hopefully.
03
Evaluate
A regression suite built from real inputs, run on every change. Without it, prompt work is superstition and every deploy is a coin flip.
04
Harden
Timeouts, retries, fallbacks to a smaller model, graceful degradation, and prompt-injection defences on anything that reads untrusted input.
05
Instrument
Latency, token spend and quality tracked per feature, so cost is a number you watch rather than a surprise at month end.
03/What's included
What you actually receive
Concrete deliverables, not a statement of intent.
Feature scoping with a defined quality bar
Schema-validated structured outputs
Evaluation harness built from your real inputs
Fallback, timeout and degradation strategy
Prompt-injection and abuse hardening
Latency, cost and quality instrumentation
Built with
- TypeScript
- Python
- Structured outputs
- Eval harnesses
- OpenTelemetry
Tooling is chosen per engagement. This is what this kind of build typically uses, not a fixed stack we sell.
04/Also in this category
AI & Intelligent Automation
The repetitive, high-volume work that quietly eats a business's week.
TriggerProcessAct
Document extraction
Lead responseLead Response & CRM Automation
New enquiries get a researched, personalised reply in minutes — not whenever somebody next opens the shared inbox.
Capture → Qualify and route → Follow up
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Client reportingClient Reporting Automation
Recurring client and management reports assemble themselves from your data sources — branded, narrated and delivered on schedule.
Connect → Compose → Deliver
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Is this the problem you have?
Tell us what it looks like at your end. We will say honestly whether this is the right fit — including when it is not.