Answers grounded in
your own data
Most wrong answers are retrieval failures, not model failures. We build the part that finds the right passage first, then makes the model quote it instead of improvising.
index of your content
with citations and access rules
to a source passage
on a warm index
What we build
Retrieval is a pipeline, not a feature. These are the parts that decide whether the answers are any good.
Source ingestion
Documents, wikis, tickets, spreadsheets and database records pulled into one index, with the connectors kept running.
Chunking and embedding
How a document is split decides what can be found. We tune it against your own content rather than accepting a default.
Hybrid retrieval
Keyword and semantic search together, because exact identifiers and part numbers still matter and embeddings are bad at them.
Citations
Every answer carries the passage it came from, so a reader can check it in one click and a reviewer can audit it later.
Freshness
Incremental sync so the index reflects what changed this morning, not what was true at launch.
Access control
Retrieval respects your permissions. Nobody gets an answer assembled from a document they could not open themselves.
How it works
When retrieval finds nothing relevant, the honest answer is that there is nothing relevant. We build for that case explicitly.
Where knowledge lives
- Docs, wikis, PDFs
- Tickets and email threads
- Product database records
- Existing search index
The retrieval layer
- Connectors and incremental sync
- Chunking tuned to your content
- Hybrid keyword + semantic search
- Re-ranking and citation
- Permission filtering
Where it surfaces
- Search box in your product
- Assistant or copilot
- Internal tools and dashboards
- API for your own use
How we work
Retrieval quality is measurable. We measure it before anyone calls it done.
Map the sources
A week working out where the knowledge actually is, what is stale, what is duplicated and what nobody is allowed to see.
Build the index
Connectors, chunking and embedding on your real content. At the end of this you can search it and judge the results yourself.
Tune retrieval
Hybrid search and re-ranking, measured against a set of real questions with known correct sources. This is where most of the gain is.
Wire in permissions
Retrieval filtered by your existing access rules, tested with accounts at different permission levels.
Operate
Query logs, gaps where nothing was found, and a monthly review of what people asked that the index could not answer.
Built by a team that ships
The AI layer is new. The engineering underneath it is not. These are products we designed, built and still maintain.
Data collection at scale
A digital market study and data-collection platform built for fast, reliable research at scale.
Product development · Frontend
IT and software services
Tooling for teams
Life in Codes helps teams work more efficiently, implementing collaboration and code-quality tooling as an Atlassian partner.
UX/UI · FrontendUsable for everyone
AccessGO is a digital accessibility platform that helps businesses make their products usable for everyone.
Web development · AccessibilityThe choices that matter
Retrieval systems fail in specific, predictable ways. These are the decisions that determine which failures you get.
Native connectors, a scheduled crawl, or a push API
Whatever keeps the index current with the least operational burden on your team. A stale index is worse than no index.
Fixed, semantic or structure-aware
Structure-aware wins on documents with real headings and tables. We test on your own files before committing.
pgvector, Qdrant, or the search you already run
Usually whatever your database already runs. Hybrid search almost always beats pure vector search on real queries.
Reciprocal rank fusion, then a cross-encoder re-rank
Re-ranking costs a little latency and buys a lot of precision. Worth it on anything a customer sees.
Filtering at index time, or at query time
Query-time is safer and is what we default to. Permissions change more often than documents do.
“Inventiff.’s quality of work and communication are unique.”
Founder & COO, fashion brand Verified review on ClutchGot an idea? Let’s make it real.
Tell us the short version
This could be the first step towards a new and successful collaboration. A one-line idea and a finished spec are both fine — tell us the problem, the deadline you’re working to and what’s in your way.
Frequently asked questions
The model is not asked to remember your business. It is given the relevant passages from your own documents at the moment of the question, and asked to answer from those. That is why the answers can carry a citation.
Fine-tuning teaches style and format well, but it is a poor way to store facts that change. Retrieval keeps the knowledge in a place you can update, audit and delete from, which matters more for most businesses.
Documents, wikis, PDFs, tickets, email threads, spreadsheets and database records. If it has an API or sits in a folder, it can usually be indexed and kept in sync.
Yes. Retrieval is filtered at query time by your existing access rules, so nobody receives an answer assembled from a document they could not open themselves.
We build a set of real questions with known correct sources and measure against it. Retrieval quality is the single biggest driver of answer quality, and it is measurable long before anyone judges the wording.
The system says so. We treat that as a designed outcome rather than an edge case, and log it, because the questions the index cannot answer are the most useful thing it produces.