Service
Your team's expertise lives in a dozen different systems. We consolidate it into one AI-searchable layer so asking "what do we know about X" gets a real, cited answer in seconds.
Wikis, Google Drive, Notion, Confluence, SharePoint, old PDFs, Slack threads, support tickets, RFP responses. Midus ingests the whole mess, chunks and embeds it, and puts a retrieval layer in front of it — exposed as an API, a chat UI, or surfaced directly inside the tools your team already uses.
Who this is for
The people who know how things work are three senior staff and a handful of Slack threads. When they're on vacation, projects stall. When they leave, institutional memory walks out with them. Nothing is written down in one findable place.
Policies in Confluence, product docs in Notion, sales collateral in Drive, contracts in SharePoint, support history in Zendesk, ops runbooks in a GitHub wiki. Native search in each system finds a fraction of what's relevant, and nothing cross-references.
You wrote the onboarding guide last quarter. Half of it is already wrong. New hires learn by asking, interrupting, and guessing. The "source of truth" has drifted so far from reality that nobody trusts it anymore.
What we build
Every deployment is custom. The components below are what we typically ship together; the mix depends on how many sources you have, their access controls, and where the answers need to surface.
Connectors for Google Drive, Notion, Confluence, SharePoint, GitHub wikis, Zendesk, Intercom, Slack, email archives, and raw PDF/DOCX folders. We also handle the weird stuff — legacy shared drives, exported databases, scanned documents that need OCR.
Documents get split along meaningful boundaries — sections, tables, code blocks — not arbitrary character counts. Each chunk is embedded into a vector index tuned for your domain, so retrieval pulls the right passage, not just the right document.
Every answer links back to the source document, section, and paragraph it came from. If the system can't find supporting content, it says so instead of inventing one. Nobody has to trust the answer blind — they can click through and verify.
We respect the access rules your source systems already enforce. HR docs stay visible only to people who could already read them. Customer records stay scoped to the accounts a rep owns. The index doesn't become a shortcut around your permission model.
The index updates continuously as sources change. Stale pages are flagged — last-modified date, contradicted by newer content, or no reader in six months. You get a dashboard of what's rotting so you can prune or refresh it.
The index isn't just a chat box. It's a retrieval API that powers custom assistants, in-app search, onboarding tools, sales enablement surfaces, and anything else that needs grounded answers. Build once, use everywhere.
Outcomes
Example scenarios below reflect the range we typically see. Your numbers will depend on content quality, source count, and how actively the team adopts the new interface.
Onboarding ramp
When a new engineer or CS rep can ask "how do we handle refund disputes" and get a cited answer pulled from the actual policy and three past tickets, the ramp phase compresses from months to weeks. Senior staff stop being the help desk.
Example scenario.
Support resolution time
Support agents spend less time hunting through wikis and pinging Slack, more time actually responding. Teams we work with typically see 20–35% reductions in average handle time on knowledge-bound tickets.
Example scenario.
Sales velocity
A sales engineer on a live call asks "does our integration support SAML with Okta groups" and gets a cited, accurate answer in seconds. Deals don't stall waiting for a follow-up email. Win rates lift where speed-to-answer matters.
Example scenario.
How we work
We catalog every system that holds knowledge, map how permissions work in each, and flag the content that matters most. Output is a written plan: what gets indexed, what doesn't, how access is enforced, and what the team expects to be able to ask.
We stand up connectors, run initial ingestion, tune chunking and embedding for your content, and build an evaluation set of real questions the team should be able to answer. You see retrieval quality before anyone gets exposed to the system.
The index ships behind a chat UI, a search API, and integrations into Slack, Teams, or your existing tools. We pilot with one team first, watch the queries, fix the gaps, then roll out across the company.
FAQ
Native search in Notion, Confluence, or Drive only finds what's in that one system, and it matches keywords, not meaning. Glean and similar enterprise search tools are closer — they do cross-system retrieval — but they're closed products with fixed behavior. What we build is a bespoke layer we own with you: tuned to your content, wired into your own assistants and products, and modifiable when your needs change. If Glean fits, use Glean. If it doesn't, this is the alternative.
Permissions are honored from the source systems, not flattened into a free-for-all index. At query time we check what the asking user can already see, and filter retrieval to those documents before the model ever gets them. This is the single most complex part of most knowledge projects and we scope it carefully — inherited permissions, group memberships, and external sharing all need handling.
Depends on the source. Webhook-capable systems (Notion, Confluence, Slack) update in near real time. Polled sources (older drives, email archives) typically refresh on a schedule — hourly or daily, your call. A document edited at 9am is retrievable by 9:05am in most deployments.
This happens often, and there's no perfect fix. The system flags it: when retrieval pulls conflicting passages, the answer surfaces both, cites both, and notes the disagreement instead of silently picking one. We also build a staleness signal — older docs that contradict newer ones get down-weighted. Ultimately a human still has to decide which one is right, but at least the contradiction becomes visible instead of hidden.
That's your choice. We can run end-to-end on infrastructure you control — embeddings, vector store, and model inference all hosted on your cloud account or on-prem. We can also use commercial APIs (Anthropic, OpenAI) with zero-retention agreements for the model layer while keeping the index itself in your environment. We walk through the trade-offs during scoping; sensitivity and regulatory posture drive the decision.
Three cost buckets: storage for the vector index (usually modest, tens of dollars a month for most companies), embedding refresh (one-time heavy, then incremental), and query-time model inference. For a typical mid-market deployment with 100k–1M documents and steady internal usage, expect $500–$3,000/month in total infrastructure cost. We spec realistic numbers during scoping.
Related services
Tell us where your content lives, who needs to query it, and what permissions have to hold. We'll come back with a realistic plan, cost range, and timeline — typically within two business days.