Inspection reportingAI Inspection & Defect Reporting
Photograph a site; get a structured, drafted defect report before you are back in the van.
Capture → Classify → Review and issue
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Internal Knowledge Assistant (RAG)
A private assistant that answers from your own documents, with citations, without handing them to a third-party AI vendor.
- Time to value
- Working pilot on a real corpus in 3 weeks.
- Deployment
- Can run fully on your infrastructure
- Category
- Data, Knowledge & Documents
0 bytes
leave your network on the on-prem deployment
The manual version of thisInvictt AI layer
Ingest
Index
Retrieve
Answer
Govern
Once it's AI-assisted01/The problem
The answer exists. It is in a policy PDF, a contract from 2019, a Slack thread, or one colleague's head. Finding it costs twenty minutes and somebody else's afternoon — and the moment anyone suggests pasting it into a public chatbot, legal quite reasonably says no.
02/See it in action
What you'd actually see
A demonstration of the mechanism — not a live system reading your data.
Internal knowledge assistant
“What is the policy for Stage II documentation?”
03/How it works
The operating model
What enters, what the system does with it, and what reaches you.
Input
Ingest
Connectors to SharePoint, Google Drive, Confluence, Notion, S3, or a plain network folder. Scanned PDFs and tables are parsed properly rather than flattened into unusable text.
System
Index
Documents are chunked along their actual structure — sections, clauses, table rows — rather than every 500 characters, which is the single biggest determinant of whether retrieval works.
System
Retrieve
Hybrid semantic and keyword search with a reranking pass, so an exact policy number and a vaguely-worded question both find the right page.
Outcome
Answer
Every answer carries inline citations to the source document and page. If the corpus does not contain the answer, it says so instead of producing a confident invention.
Check
Govern
Retrieval is permission-aware: it mirrors the access rules you already have, so nobody discovers a salary band through the chat interface.
Read the detail behind each stage
01
Ingest
Connectors to SharePoint, Google Drive, Confluence, Notion, S3, or a plain network folder. Scanned PDFs and tables are parsed properly rather than flattened into unusable text.
02
Index
Documents are chunked along their actual structure — sections, clauses, table rows — rather than every 500 characters, which is the single biggest determinant of whether retrieval works.
03
Retrieve
Hybrid semantic and keyword search with a reranking pass, so an exact policy number and a vaguely-worded question both find the right page.
04
Answer
Every answer carries inline citations to the source document and page. If the corpus does not contain the answer, it says so instead of producing a confident invention.
05
Govern
Retrieval is permission-aware: it mirrors the access rules you already have, so nobody discovers a salary band through the chat interface.
04/What's included
What you actually receive
Concrete deliverables, not a statement of intent.
Connectors for SharePoint, Drive, Confluence, Notion, S3 or a network folder
Structure-aware parsing including scanned PDFs and tables
Hybrid retrieval with reranking
Inline citations to source document and page on every answer
Permission-aware retrieval mirroring your existing access control
Fully on-prem deployment option — no data leaves your network
An evaluation set built from your team's real questions
Usage analytics showing what people ask and where the corpus is thin
Built with
- Qdrant / pgvector
- Ollama / vLLM
- LangGraph
- FastAPI
- Rerankers
Tooling is chosen per engagement. This is what this kind of build typically uses, not a fixed stack we sell.
05/Example work
What this looks like in practice
Reference implementations, including what went wrong.
06/Also in this category
Data, Knowledge & Documents
Unstructured records turned searchable, verifiable and actionable.
IngestIndexAnswer
Inspection reporting
Detection & trackingObject Detection & Tracking
Counting, monitoring and safety checks from camera feeds, running on the edge rather than streaming everything to a cloud.
Assess → Train → Alert and log
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Classification & segmentationImage Classification & Segmentation
Custom vision pipelines for the visual data that only exists in your industry.
Define the classes → Label efficiently → Deploy
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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.