Grounded in your data
Answers come from your own conversations and documents through a retrieval index — never from a generic model guessing.
Proof layer Every answer streams with inline citations back to the source message.
AI Productivity
Turn team knowledge into instant answers
Slack-native knowledge assistants and productivity tools — hybrid retrieval over your own conversations and documents, with every answer cited back to its source.
- Hybrid search
- Your data
- RAG-powered
- Cited answers
SlackDocsCalendarEmail IngestLive eventsIndexpgvectorRetrieveHybrid + rerankAnswerCited stream
Knowledge Ops
Live What we build
4 commitments, 1 senior bench.
Every knowledge-assistant build ships against the same four constraints — no exceptions for demos or deadlines.
Native to where work happens
Slack Events API ingestion, calendar and document APIs — the assistant lives inside the tools your team already uses, not another tab.
Proof layer Integration scoped and tested against your workspace, not a mock.
Permissions-aware retrieval
Row-level security on indexed data means employees only surface knowledge from channels and documents they can already see.
Proof layer Access boundaries mapped in writing before a single message is indexed.
Senior engineers, weekly demos
A named senior lead owns the outcome. You see working retrieval weekly, not a status deck monthly.
Proof layer Senior review on every merge; 30-day exit on embedded pods.
Integration reality
Slack and collaboration platforms Vector databases (pgvector) Calendar and scheduling APIs Document stores
Where it pays back
The knowledge-loss patterns we see in every team.
- Workflow · 01
Institutional knowledge lives in Slack, nowhere else
A decision made six months ago is buried in a thread nobody can find.
A Slack-native assistant that ingests history and answers in plain language
- Workflow · 02
Employees re-derive answers that already exist
The same question gets asked and re-researched every quarter because nobody can retrieve the original context.
Semantic search over your own conversations, with reranked results
- Workflow · 03
Productivity work is scattered across disconnected apps
Chat, calendar, and document creation live in separate tools, so context gets lost switching between them.
One assistant surface for chat, scheduling, and document work
Use cases
Where internal teams put us to work.
Eight workflows where a knowledge or productivity assistant pays back first — each scoped, evaluated, and capped before it touches production.
-
Slack knowledge assistant
Workspace history ingested into a searchable index — plain-language questions answered with citations to the original thread.
Teams whose decisions live in Slack -
Hybrid semantic search
Full-text plus vector search with AI reranking, so acronyms and company-specific terms surface the right answer first.
Orgs where keyword search fails -
Institutional memory
Decisions, rationale, and technical context preserved and retrievable long after the people who made them move on.
Fast-growing or high-turnover teams -
Document drafting & summarization
Meeting notes, briefs, and recaps drafted from source material for human review — never auto-published.
Teams drowning in write-ups -
Scheduling assistance
Availability checks and meeting coordination handled inside the same assistant surface as chat and documents.
Calendar-tetris organizations -
Onboarding acceleration
New hires ask the assistant instead of interrupting seniors — ramp questions answered from real project history.
Teams onboarding every month -
Continuous ingestion pipelines
New messages and documents indexed in near real time via event APIs and async queues — the knowledge base never goes stale.
High-volume workspaces -
Access-controlled retrieval
Row-level security on the index so private channels stay private — retrieval respects existing permissions.
Security-conscious orgs
Case studies
Proven solutions. Real-world impact.
How our teams turn scattered conversations, inboxes, and documents into searchable, working knowledge through strategy, engineering, and innovation.
-
100% answers with citations AskTiz – Internal AI Knowledge Assistant for Slack
A Slack workspace turned into a searchable knowledge base — hybrid search, AI reranking, and streamed answers with inline citations to the original messages.
Read story -
-
0 manual data entry steps Gmail AI Extractor – Automated Email-to-CRM Data Pipeline
Inbound email parsed by AI and written into the CRM automatically, so reps stop copy-pasting context between inbox and pipeline.
Read story -
-
1 review surface for contracts LexReview – AI-Powered Contract Review Platform
AI-assisted contract review that surfaces risk and key clauses for human sign-off, cutting the reading burden on legal teams.
Read story -
-
Auto insight delivery AI-Powered Client Insights Automation with Zapier
Client data distilled into AI-generated insights delivered to the team automatically, replacing manual research and recap work.
Read story -
-
1 workspace, many tools replaced Creative OS – AI-Powered Creative Workspace
An AI-powered creative workspace that unifies generation, editing, and asset management in one interface instead of scattered tools.
Read story -
Capabilities
6 service lines, 1 senior bench.
Six connected capabilities, delivered as one seamless service.
See full capability matrix →AI consulting and readiness
A build decision in 2 weeks, not a slide deck.
AuditUse-case scoringRoadmap
Explore AI readiness Agentic engineering
Agents with evaluations, approval gates, cost ceilings, and a full audit trail.
RAGTool useVoiceGovernance
Explore agentic engineering Forward deployed engineering
A named senior engineer inside your team, owning an outcome.
EmbeddedWeekly demoNamed lead
Explore forward deployed engineering Custom software, web and mobile
Specification first, AI-assisted, senior review on every merge.
Next.jsNestJSFlutterReact Native
Explore custom software Automation and systems
Workflows wired and instrumented, with the payback counted.
n8nMakeCRMBack office
Explore automation Data and platform
Pipelines and lineage your AI can actually rely on.
WarehouseETLLineageObservability
Explore data & platform FAQ
The questions internal-tools owners ask first.
Straight answers before the first call. If yours is not here, ask it on the call — we answer the hard ones first.
Ask the hard one Does the assistant answer from our own data, or a generic model?
Your data. We ingest your conversations and documents into a retrieval index, so answers are grounded in what your team actually said and wrote.
What is hybrid search with reranking?
Semantic (meaning-based) search combined with keyword matching, then a reranking pass over the results so the most relevant answer surfaces first, not just the closest embedding.
Where does our conversation data live?
Your environment or a dedicated tenant, mapped in writing before anything is built.
Can employees see answers from channels they cannot access?
No. Retrieval enforces row-level security on the index, so the assistant only surfaces knowledge from channels and documents the asking employee is already authorized to see.
How does the knowledge base stay current?
New messages and documents are ingested continuously through event APIs and an asynchronous processing queue, so the index updates in near real time without rebuilding.
Start the conversation
StoplosingdecisionstoaSlackthread
nobody can find
Tell us where your team wastes time re-deriving context that already exists. We will tell you what a retrieval build looks like on your own data.
30 minutes the engineer who leads delivery no deck, no pitch