The same questions, forever
“Where do I find X?” “Who owns Y?” Every new hire asks them. Every expert answers them again. Institutional memory walks out the door when people leave.
Solution · Enterprise Knowledge Base
Every answer your company already has, in one place.
Accelyze connects to the tools your teams already use, understands what lives inside them, and answers questions with citations you can trust. No migration. No single source of truth to maintain. No behavior change for the people creating the knowledge.
Answering thousands of questions a day for engineering, operations, and support teams. Used by humans, automations, and agents.
The single source of truth is a myth
Every quarter someone proposes the same fix: move everything into one platform. It never holds. People write where it is convenient. Decisions land in Slack threads, fixes land in pull requests, context lands in a doc comment nobody will ever find again.
Search that misses the point
Keyword search cannot find an answer written in different words. Pure semantic search buries the exact error string you pasted. Both surface stale threads describing infrastructure that no longer exists.
Migration projects that stall
Consolidation tools ask teams to change how they work. They don’t. The data stays scattered, and the expensive new system becomes another place nobody looks.
How Accelyze turns scattered data into trustworthy answers
One pipeline runs behind every question. Each stage exists to fix a specific failure mode of naive search.
Sources
Slack, wikis, code, incidents, tickets, and your own databases. One connector per source, no data movement.
Distillation
LLM extractors turn raw, messy content into a normalized document: the real question, the summary, the resolution, the systems involved.
Embeddings
Everything lands in one shared vector store with high-dimensional embeddings and fast approximate nearest-neighbor search.
Retrieval
Multiple retrievers run in parallel: full-text, semantic, rarity-weighted, and recency-aware, each covering the others’ blind spots.
Fusion & rerank
Reciprocal rank fusion merges the lists, then a reranker model scores each candidate against the actual question and keeps the best.
Synthesis
A final pass writes the answer with inline citations, caveats, and cross-source context, so people can verify every claim.
Meets your data where it already lives
Accelyze extracts from each platform directly instead of asking teams to change how they work. Every source lands in the same store and becomes queryable through the same interface.
Slack & chat
Where the most current engineering discussion actually happens.
Wiki & docs
Confluence, Notion, Google Docs, and internal doc spaces.
Code repositories
Function-level and file-level understanding, kept current on every commit.
Tickets & incidents
Jira, Linear, PagerDuty, and your incident history.
Product & design docs
Specs, PRDs, and reference material scattered across tools.
Custom databases
Your own tables, made queryable without moving a single row.
Each source defines what its data is, how to connect, and how often to
refresh. A busy incident channel can sync every few minutes; a stable wiki
can sync daily. You decide, per source.
Retrieval that actually finds the answer
No single scoring method is trusted on its own. Accelyze runs several in parallel and fuses them, so a strong signal in one view is never buried by a weak one in another.
Full-text
Catches the exact tokens embeddings blur together: error strings, flag names, host names. When someone pastes a literal error, an exact match is almost always the best evidence.
Semantic
Catches paraphrase. Vector similarity connects a question to an answer written in entirely different words.
Rarity weighting
Separates signal from filler. A short message built around a rare config flag ranks; generic acknowledgements score near zero once term rarity is applied.
Recency decay
Answers expire. When relevance is otherwise equal, the newer answer wins, so results don’t describe infrastructure that no longer exists.
Fusion, reranking, and context restoration
The retrievers’ lists are merged with reciprocal rank fusion, so consensus across several views beats a single strong vote. Duplicate chunks collapse to one source, and no single file can flood the results. A reranker model then scores the finalists against the real question. For the winners, Accelyze pulls back the neighboring context, so you get the heading, preconditions, and caveats, not a lonely paragraph.
Understands messy human data, not just clean documents
Raw conversation is hard to search. Information density swings wildly, short messages beat long ones for the wrong reasons, and meaning depends on the thread around it. Accelyze does the work to fix that.
Thread distillation
For every conversation, an extractor pulls out a searchable question, a short summary, the resolution, and the systems and code referenced. The normalized version is what gets embedded.
Bursting
Sometimes the answer lives in one tangent message whose vocabulary never reaches the summary. Accelyze embeds high-signal runs of messages on their own, so that message stays findable.
Signal thresholds
Low-value chatter never reaches the index. Content must clear a bar: rare tokens, real length, or a social signal like a reaction.
Always the full conversation
A reply re-pulls its parent and every sibling, so the stored record, the participant list, and the last-activity timestamp always reflect the complete, current thread.
Code-aware, and always current
Grep finds the string. Accelyze finds the intent. Semantic search over your repositories connects “how do we retry failed uploads” to the function that does it, even when the words never match.
Multi-level chunking
Code is split along language-aware boundaries, from class to method to block, so a file is searchable at file, class, and function level.
Incremental on every commit
Only changed chunks are re-embedded. Large monorepos stay current without recomputing the world, even across tens of gigabytes.
Recent pull requests
Ask what changed and why. Accelyze surfaces the relevant PRs alongside the discussion that produced them.
Self-serve onboarding
Teams add repositories through config files, with path-level allowlists and denylists, so nothing sensitive is indexed by accident.
Build your own connector in an afternoon
Any source that can emit rows in the shared schema becomes queryable alongside everything else, with no special handling anywhere downstream.
A team opens a pull request with a small module that reads from their system and writes rows shaped like every other record: a document, an embedding, and metadata. That is it. The planner, retrieval, reranking, and synthesis all work unchanged. The interface is deliberately narrow so it can absorb almost any kind of data: chat, documents, code, structured tables, telemetry.
Built for agents, not just people
Accelyze exposes its retrieval as clean, composable tools over MCP and a typed API. Your agents call the same primitives the product uses internally, and orchestrate them however they need.
Retrieval primitives, not a black box
Each tool maps to one operation: semantic search, lexical search, code search, expertise lookup. Inputs and outputs are narrow, structured, and stable.
Your agent is the orchestrator
Claude Code or any MCP-compatible agent decides which tools to call, in what order, and how to assemble the result.
Full pipeline over the API
Prefer one call? The end-to-end planner, executor, and synthesizer are available as a single endpoint that returns an answer with citations.
Same guarantees everywhere
Every path enforces the same permissions, auditing, and scoping. Programmatic access is not a side door around your controls.
Relevant by default with projects
“Search everything everywhere” stops being useful fast. Projects make search relevant without any effort from the user.
A project is a named bundle of sources: specific channels, repositories, databases, and doc spaces relevant to a team or initiative. Sources can be shared across projects instead of duplicated. During onboarding, each person picks or creates a default project that scopes their queries automatically, so a new hire gets high-signal answers on day one.
Permission-aware by default
A knowledge base is only safe if it never shows someone a document they could not open at the source. Accelyze enforces access at query time, not as an afterthought.
Source permissions, respected
Every result is checked against the permissions of the underlying system. Restricted channels, private repos, and gated doc spaces stay invisible to people who lack access.
Per-user, per-query
Retrieval is filtered for the person asking. Two people can ask the same question and correctly get different evidence.
No leakage through synthesis
The synthesizer can only cite what the user is authorized to retrieve. An answer can never quote a source the reader was not permitted to open.
Full audit trail
Every query, every source touched, and every answer returned is logged for review, compliance, and investigation.
Enterprise security and compliance
Built for teams that have to answer to security reviews, auditors, and regulators.
SOC 2 Type II
Independently audited controls for security, availability, and confidentiality.
ISO 27001
A certified information security management system across the platform.
GDPR & data residency
Region-pinned storage and processing, with data-subject controls.
HIPAA-ready
BAAs available for teams handling protected health information.
SSO & SCIM
SAML and OIDC single sign-on, with automated provisioning and deprovisioning.
Encryption everywhere
Encrypted in transit and at rest, with support for customer-managed keys.
No training on your data
Your content is never used to train foundation models. Ever.
PII controls
Optional redaction and configurable retention windows per source.
Deploy the way your security team requires
The same product, on the infrastructure you are comfortable with.
Cloud
Fully managed and multi-tenant. The fastest path to value, with nothing to run.
Dedicated VPC
A single-tenant instance in your cloud account, isolated network and storage, managed by us.
Self-hosted
Run the whole stack inside your own perimeter for the strictest environments. Air-gapped options available.
Bring your own model
Point distillation, reranking, and synthesis at your preferred provider or a private endpoint.
See what your organization is really asking
Accelyze is also a map of your institutional knowledge and its gaps.
Knowledge gap detection
Surface the questions people ask that have no good answer yet, so teams know exactly what to document next.
Expertise graph
Find the person with demonstrated knowledge on a topic, not just a title. Route hard questions to whoever solved them last time.
Usage analytics
Adoption by team, top topics, deflected repeat questions, and where answers come from. Measure the value, not just the traffic.
Answer quality feedback
Ratings and corrections flow back into tuning, so the system gets sharper on the questions that matter to you.
Source freshness controls
Set refresh cadence per source, reset watermarks, and force re-ingestion from one console.
Granular admin roles
Control who can add sources, create projects, view analytics, and manage access.
Under the hood
Architecture
Accelyze ingests data from where it already lives, normalizes it into a single vector store, and answers questions with cited evidence. The core is small on purpose: complexity lives in ingestion and retrieval, not in a sprawling schema.
Design principles
Meet data where it lives
Extract from each platform directly. Migration projects fail; ingestion does not.
One schema, one store
Heterogeneous sources collapse into a single row shape. Anything that fits is immediately queryable.
No single scorer is trusted
Every retrieval method has a failure mode. Run several in parallel and fuse them.
Primitives over black boxes
Expose retrieval as small, stable, mostly LLM-free tools. Let clients and agents orchestrate.
Permissions are not optional
Access is enforced at query time against the source of truth, for every path including the API.
The data model
At the center is a single Postgres table backed by pgvector. Every source writes rows in the same shape.
- document
- The normalized text that was embedded. Not the raw transcript.
- embedding
- A high-dimensional vector (3072-dim), indexed with HNSW for fast approximate nearest-neighbor search.
- source
- The source type, e.g. slack_thread, code_chunk, wiki_section, custom.
- source_id
- A stable identifier for upserts and deduplication.
- metadata
- Channel or repo, authors, timestamps, permissions handle, and source-specific fields.
- raw_summary
- The distilled artifact retained for display and reranking context.
Query pipeline
Every question runs through a planner, an executor, and a synthesizer.
- 1
Planner
A lightweight pass inspects the query and active project, then chooses which tools and sources are likely to matter.
- 2
Executor
The chosen tool calls fan out in parallel. Results are normalized into a shared evidence schema carrying scores, recency, and source hints.
- 3
Synthesis
A final pass takes the typed evidence bundle and the question, then produces the answer with inline citations and cross-source reconciliation.
Retrievers are fused with reciprocal rank fusion: for each document, add
weight / (60 + rank)
across every list it appears in. The smoothing constant of 60 makes
consensus matter more than a single strong vote.
Retrieval tools
Retrieval is exposed as direct MCP tools rather than hidden behind a single answer endpoint.
- search
- Unified vector pipeline across all indexed sources, merged and reranked internally.
- search_chat
- Direct chat retrieval.
- search_code
- Ripgrep over source repositories.
- subsystem_index
- Per-file summaries for orientation.
- recent_prs
- Recent pull requests relevant to the question.
- who_knows
- People with demonstrated expertise on a topic.
Scaling & performance
- HNSW indexing keeps nearest-neighbor search fast as the corpus grows into the tens of millions of vectors.
- Retrievers run concurrently, so added methods cost latency roughly equal to the slowest one, not their sum.
- Incremental code embedding means repository size affects storage, not per-commit compute.
- Reranking runs on a capped candidate set, so its cost stays bounded regardless of how many documents matched.
- Project scoping shrinks the planner and retrieval working set, cutting both cost and latency for scoped queries.
Failure modes and guarantees
A source goes down
Ingestion for that source pauses and resumes from its watermark. Everything already indexed stays queryable. No partial-write corruption.
The reranker is unavailable
The pipeline degrades to fused rankings. Answers get less precise, not absent.
Permissions cannot be resolved
The system fails closed. When access is uncertain, the row is withheld, never shown.
No answer is well-supported
Synthesis says so and cites what little it found, rather than fabricating confidence. Weak queries also feed gap detection.
Connects to the tools you already run
A growing catalog of first-party connectors, plus an open interface for anything not on the list yet.
Slack Microsoft Teams Confluence Notion Google Drive GitHub GitLab Jira Linear PagerDuty Zendesk SharePoint Salesforce Custom databases
Need one we don’t list? Custom connectors ship in a single pull request.
Who uses Accelyze
Engineering
Debug faster with answers drawn from Slack, code, PRs, and past incidents at once. Onboard new engineers in days, not months.
Support & success
Resolve tickets with the same knowledge your best agents carry in their heads, cited and current.
Operations & IT
Turn runbooks, policies, and past incidents into instant, permission-safe answers for the whole company.
Platform teams
Expose retrieval to internal agents and automations over MCP, and build custom connectors for your own systems.
How Accelyze compares
Most enterprise search tools answer questions. The differences show up in how open they are, where they can run, and whether your agents can build on them. Here is an honest read.
✓ Strong → Partial / varies – Not really
| Capability | Accelyze | Suite platforms (e.g. Glean) | Ecosystem search (e.g. Microsoft Copilot) | Wiki + AI (e.g. Notion, Guru) | Build it yourself |
|---|---|---|---|---|---|
| Connects to data in place, no migration | ✓ Yes, one connector per source | ✓ Yes | → Strongest inside its own ecosystem | → Mostly its own content | – You build every connector |
| Permission-aware retrieval | ✓ Yes, enforced per user at query time | ✓ Yes | ✓ Yes | → Varies by tool | – You build it, and it is the hard part |
| Explainable hybrid retrieval | ✓ Yes, documented and tunable | → Proprietary and opaque | → Proprietary | → Basic semantic search | → Yours to design and maintain |
| Function-level code search | ✓ Yes, incremental on every commit | → Limited | – Not a focus | – No | → Possible, significant effort |
| Custom connectors on an open schema | ✓ Yes, a small module and one pull request | → SDK and plugins, closed product | → Graph connectors, within the platform | – Limited | ✓ That is the whole project |
| Agent-native retrieval over MCP | ✓ Yes, primitives your agents orchestrate | → Proprietary agent framework | → Proprietary agent framework | – No | → You expose your own |
| Self-hosted and air-gapped deployment | ✓ Yes, including air-gapped | → Managed SaaS, limited self-host | – Cloud only | – Cloud only | ✓ Runs wherever you run it |
| Bring your own model | ✓ Yes, any provider or private endpoint | → Model choice within their platform | – Tied to the vendor’s models | → Varies | ✓ Your choice |
| No training on your data | ✓ Yes | ✓ Yes | ✓ Yes | → Varies by tool | ✓ Your policy |
| Time to first answer | ✓ Days | → Weeks, guided rollout | ✓ Fast inside the ecosystem | ✓ Fast, narrow scope | – Months of engineering |
| Knowledge-gap analytics and expertise graph | ✓ Yes | → Partial | – No | – No | – You build it |
Where Accelyze is actually different
The mature platforms already respect permissions and keep your data out of model training. We do too. The line that separates us is openness and control.
Open, not walled
Any source that emits rows in our shared schema is queryable. Your team owns its connectors instead of waiting on a roadmap.
Runs where you require
Managed cloud, dedicated VPC, or fully self-hosted and air-gapped. The same product, inside whatever boundary your security team demands.
Agents are first-class
Retrieval is exposed as clean MCP primitives. Your agents orchestrate them directly instead of being locked into one vendor’s framework.
Retrieval you can reason about
Lexical, semantic, rarity, and recency signals fused with a method you can inspect and tune, not a black box you have to trust blindly.
Comparison reflects general product positioning as of mid-2026. Vendor capabilities change often. Confirm current specifics with each provider before a purchase decision.
Coming soon
Get notified of release
Open Core: transparency you can verify, control you can trust
We’re releasing an Open Core that puts transparency, extensibility, and technical sovereignty in your hands. Same enterprise-grade features, your infrastructure, full control.
Core self-deployment
Deploy the retrieval engine, connectors, and hybrid search on your own infrastructure.
Modification rights
Fork, customize, and extend to fit your specific requirements and workflows.
Community innovation
Benefit from shared connectors, tools, and improvements developed by the community.
Enterprise additions
Enterprise connectors, compliance certifications, SLAs, and dedicated support remain premium.
Common questions
Do we have to move our data?
No. Accelyze reads from each source in place and keeps its own query-ready index in sync. Your teams keep working exactly as they do today.
Will it show people things they shouldn’t see?
No. Retrieval respects the permissions of the underlying source, filtered per user at query time. If you can’t open it there, it won’t appear here.
Do you train models on our content?
Never. Your data is used only to answer your team’s questions. It does not train any foundation model.
Can we run it in our own environment?
Yes. Choose managed cloud, a dedicated VPC in your account, or a fully self-hosted deployment, including air-gapped setups.
How current are the answers?
Sources refresh on a cadence you control, per source. Busy channels can sync in near real time, and recency decay keeps stale answers from winning.
What if you don’t have a connector for our system?
Any source that can emit rows in our shared schema becomes queryable. Custom connectors are a small module and a single pull request.
Can our agents use it?
Yes. Retrieval is exposed as clean tools over MCP and a typed API, so your agents and automations query the same knowledge your people do.
Stop answering the same question twice
Give every person and every agent in your company one place to ask, with answers drawn from everything you already know.
Book a demo