KognitaKognita.
Kognita

Semantic Code Intelligence

Managed Agent Runtime

Your codebase has all the answers.
Only a few people can read it.

Kognita turns your system into a shared knowledge layer — so your whole team, and the AI tools they use, can ask questions and get answers grounded in real code.

Start for free↗For DevsNon-Technical

2-week free trial · no card required

Kognitakognita
kognita · in 30s
Three people. Three angles. One broken system.

plain-language chat

What changes if we modify this workflow?
It affects ownership, validation, notifications, and reporting. The riskiest part is changing the rule before the surrounding teams understand the impact.

for adoption

AI adoption fails when teams
do not share context.

Kognita gives developers, AI tools, product teams, support, operations, and leadership a shared language for the system. People can ask better questions, prepare better work, and understand risks earlier.

system behaviorrelease riskonboardingownershipuser impact

KPIs by role

What changes
for the people who use it.

Start with the human change: what gets easier for each team. Then show the KPI needles Kognita moves because that work changed.

  • 01

    Developers

    engineering · AI coding tools

    Before: Every AI session has to rediscover the architecture before useful work starts.

    After: Build with the whole system in context — without rebuilding it every session.

    “I can onboard into a service and trace the real code path before I edit.”

    What goes down

    02
    • Context-rediscovery time per session
    • AI rework and PR churn

    What goes up

    02
    • First-shot AI plan accuracy
    • Cross-service changes shipped with confidence
  • 02

    Product

    PMs · product owners

    Before: Feature scope depends on memory, old docs, and another engineering walkthrough.

    After: Scope features against real implementation, not assumptions.

    “I can check existing product behavior before I write the ticket.”

    What goes down

    02
    • Time to a grounded scoping answer
    • Spec rewrites after engineering review

    What goes up

    02
    • Cross-repo risks caught pre-sprint
    • Specs grounded in real implementation
  • 03

    Support · QA · Ops

    customer-facing teams

    Before: Issues reach engineering as vague symptoms and partial screenshots.

    After: Escalate with evidence, not guesses.

    “I can tell whether a customer issue is expected behavior or a real defect.”

    What goes down

    02
    • Triage round-trips with engineering
    • Time to reproduce a customer issue

    What goes up

    02
    • Tickets cited to real code paths
    • Defects classified correctly on first pass
  • 04

    Leadership

    CTO · VP Eng · founders

    Before: Risk, ownership, and delivery questions become custom investigations.

    After: Inspect risk, ownership, and delivery state without a custom investigation.

    “I can see release risk, customer impact, and ownership without a custom investigation.”

    What goes down

    02
    • Time to a verified system answer
    • Engineering interruption tax

    What goes up

    02
    • Visibility into release and migration risk
    • Decisions tied to current system state

The point is not another chat window. It is a visible behavior change for each role, tied back to the operational metrics the business already cares about.

the bridge between systems and consumers

How does Kognita
manage all this?

Natural language in. Precise system context out.

Kognita is a managed agent runtime built on a custom semantic engine. The engine translates your system into searchable meaning; the runtime serves it as one grounded source of truth — to your whole team in the browser, and to AI coding tools through MCP.

01code → english

First, your system gets translated into meaning.

Routes, services, jobs, queries, and business logic stop being opaque files. Kognita rewrites them as searchable concepts — what they do, who uses them, what they touch — in language anyone on the team can ask about.

Kognita turns your codebase into a semantic layer that AI coding tools, developers, and non-technical teams can query in plain language.
kognita
02meaning → relationships

Then it maps every connection between them.

Functions, callers, downstream services, Kafka events, table touches — connected as queryable edges in one graph. Developers and AI tools follow real call paths instead of guessing which repo, service, or table matters.

◆ fully mapped

Every function, import, service call, event, and table — connected.

◆ instant traversal

Real call paths, not guesses about which repo or service matters.

◆ one graph, two surfaces

Powers chat for the team and MCP for editor-side tools.

supported languages

JavaScriptTypeScriptPythonJavaC#GoRustRubyPHPHCL / Terraform

not vs — under

Cursor, Claude Code, and Codex are great in the editor.
Kognita is the layer they query.

IDE-side agents rediscover the codebase every session and only talk to developers. Kognita gives them a persistent semantic layer through MCP — and gives the rest of your team grounded answers in the browser.

editor-side agents

CursorClaudeCodex

shared semantic layer

KognitaKognita

editor tools

Editor-only

Lives inside the IDE. Help is for the developer with the file open.

kognita

Whole team

Browser-based managed runtime. PMs, support, ops, leadership all ask the same system.

editor tools

Session context

Every chat starts cold. The agent re-greps the repo to figure out where things are.

kognita

Persistent semantic layer

Services, routes, jobs, schemas, ownership, and intent — indexed once, refreshed on push.

editor tools

Code only

Reads the files in your repo. No view of Jira context, live data, or external tools.

kognita

Code + Jira + DBs + MCP

Tickets, read-only databases, and approved MCP tools sit in the same runtime as the code.

CursorClaudeCodex

And yes — we serve them too. Connect Cursor, Claude Code, Codex, or Windsurf to Kognita's MCP endpoint and they pull from the same semantic layer your team uses in the browser. One source of truth, two surfaces.

one shared system layer

Same source of truth.
The right depth for every role.

Kognita serves the same system truth at different levels of detail: implementation evidence when depth matters, plain-language answers when clarity matters.

development

code context

management

system visibility

Kognita

Kognita

shared system context

developers

MCP context for AI coding agents

Connect the repo once.
Query it from every AI tool.

Skip the bespoke RAG pipeline. Kognita exposes your repositories as an MCP-native semantic graph — call sites, routes, jobs, schemas, and prose intent — queryable from every modern dev agent.

Open developer docs
business teams

plain-language system answers

Ask the codebase a question.
Get an answer you can act on.

No terminal, no clone, no walkthrough request sitting in a backlog. Teammates outside engineering get cited, code-grounded answers about how the product actually behaves — in language they already use.

See team workflows
one system layer · role-specific answers · evidence shared across the team

one agent · every source

Connect the systems
your agent needs to act.

Kognita gives the agent the work context, live evidence, and approved actions it needs in one scoped runtime: Jira for intent, databases for truth, Zapier MCP for common apps, and custom MCPs for tools your team brings or hosts.

work context

Tickets connected to the code that ships them.

Pull epics, acceptance criteria, release history, and prior decisions into the same agent session as the implementation.

  • ↳Issue and epic context
  • ↳Acceptance criteria
  • ↳Ticket to code grounding
Open setup guide→
Databaseread-only evidence

Live records from the systems of record.

Connect named read-only databases so the agent can verify records, customer state, adoption, payments, and reports without guessing.

  • ↳Read-only users
  • ↳Named connections per project
  • ↳Plain-language analytics
Open setup guide→
Zapierapproved actions

Thousands of app actions without custom glue code.

Reach Gmail, Slack, Salesforce, Linear, Notion, calendars, and more through approved Zapier MCP tools.

  • ↳8,000+ app connections
  • ↳Scoped tool sets
  • ↳Streamable HTTP MCP
Open setup guide→
Custom MCPbring your own

Your own tools in the managed runtime.

Bring a vendor-hosted MCP or host your own public HTTPS MCP server, then connect it with Streamable HTTP or SSE.

  • ↳Self-hosted endpoints
  • ↳Encrypted headers
  • ↳Runtime tool guidance
Open setup guide→

Every connection is scoped per project and authorized per source. The agent only reaches what your team approves — code, tickets, databases, MCP tools — never anything else.

one runtime · many shapes

Pick a scenario.
Watch the runtime run it.

A few examples of what teams build on a managed agent runtime with complete understanding of their system through a unified semantic layer. The runtime is the product — these are shapes it takes.

Jira webhook → managed agent → first-response and full-resolution SLA without paging engineering.

Read the anonymized service desk AI case study
  1. T+0Ticket created

    Webhook fires from Jira on ticket creation.

    A Jira automation rule on the Support project posts the new issue to the Kognita project webhook. No human routing.

  2. T+18sContext loaded

    Agent already knows the system.

    No cold start. The project agent has the semantic layer, the Jira project, and the read-only database — and looks up the reporter in the customers table.

  3. T+1m 42sFirst-response SLA met

    Agent posts the first response on the ticket.

    Customer identified, affected feature located in code, owning service named, no broad outage signal — enough for support to set expectations with the customer immediately.

  4. T+6mInvestigation

    Traces the call graph, checks the data, finds the suspect.

    Walks the report-generation path across services, queries the read-only replica for Acme's recent jobs, correlates with the most recent deploy on reports-service.

  5. T+14mFull-resolution SLA met

    Posts root cause + two implementation options.

    Root cause in the 09:02 deploy — a query filter regressed for enterprise plans. Two fix options drafted with files, blast radius, and rollback path. Owner tagged from CODEOWNERS.

Jira · Support
sourcessemantic layerJiradatabase (read-only)
SUP-4821HIGH09:14

Weekly reports aren't loading for our team

Reported by jordan@acmecorp.com · Acme Corp · enterprise plan

Jordan @ Acme CorpT+0

POST /webhooks/kognita

"issue": "SUP-4821"

"summary": "Weekly reports aren't loading…"

"project": "Support"

Managed agentT+18s

sources loaded

  • semantic layer · 14 services
  • jira · SUP project
  • db (read-only) · production-replica
Kognita on SUP-4821T+1m 42s

Jira comment · public

Acked. Reproducing against Acme Corp's account (plan: enterprise).

Affected: reports-service → /api/reports/weekly

No related incidents in the last 24h. Investigating.

Managed agentT+6m

trace · 3 evidence points

  • ↳reports-service @ commit a1c9f2 · deployed 09:02
  • ↳db: 0 rows in report_jobs for acme_org_id since 09:02
  • ↳callers: weekly-cron, dashboard.tsx:loadReports
Kognita on SUP-4821T+14m

Jira comment · internal · @owner

Root cause: reports-service a1c9f2 — plan filter dropped enterprise.

Option A: revert commit (1 file, ~3 LOC). Fast, safe.

Option B: forward-fix in plans/filters.ts. Preserves new logic.

first-response SLA

Met in 1m 42s

full-resolution SLA

Met in 14m

Illustrative scenario. The agent acts on the systems your team has authorized — code, Jira, databases, custom MCPs — and posts every step back to the source it was triggered from.

why it matters

A personal frustration.

Every new AI chat starts cold. It knows nothing about your services, data, history, naming, failures, customers, or business rules. Asking it to help without context is like waking someone at midnight and asking them to debug a production system they have never seen.

Kognita bridges that gap.

Kognita exists to democratize code without replacing the people who build it.

Engineers do not need replacement. They need clearer requirements, less stress, fewer bugs, and fewer unhappy customers. They need AI tools that understand the system before they suggest a change.

Kognita creates a semantic code intelligence layer for complex software systems. It gives AI tools immediate context across codebases, databases, tickets, infrastructure, business logic, and production workflows, so answers are grounded in how the system actually works.

It also helps non-technical teams understand software without learning the codebase first. They are not becoming more technical. They are becoming more connected, more informed, and better able to work with engineering.

Better requirements
Less production stress
Shared system context

role based outcomes

One system map. Different answers for every team.

Kognita does not force everyone into an engineering workflow. Each role gets the same underlying system context, shaped into the questions they already need to answer.

Engineering

Where does this workflow break?

Trace the service, method, event, table, and logs before changing code.

codedatacontext

Product

Can we ship this change safely?

Check existing behavior, dependent flows, reused logic, and likely release risk.

codedatacontext

Support

Is this a bug or expected behavior?

Classify tickets earlier and suggest the right workaround, owner, or escalation path.

codedatacontext

Leadership

What is happening across the operation?

Ask questions across teams, workflows, data, and operational signals without waiting for a custom report.

codedatacontext

for developers

Plug your dev tools into the same system layer.

Kognita exposes the indexed system through MCP, so Claude Code, Cursor, Codex, and internal agents can retrieve the same code, database, and operational context.

Claude Code
terminal
$ claude mcp add-json kognita-mcp '{"type":"http","url":"https://mcp.kognita.co/projects/<project-uuid>/mcp","headers":{"Authorization":"Bearer <your-token>"}}'
Cursor / mcp.json
.cursor/mcp.json
{
  "mcpServers": {
    "kognita-mcp": {
      "type": "http",
      "url": "https://mcp.kognita.co/projects/<project-uuid>/mcp",
      "headers": {
        "Authorization": "Bearer <your-token>"
      }
    }
  }
}
Codex / config.yaml
~/.codex/config.yaml
mcp_servers:
  kognita:
    type: http
    url: https://mcp.kognita.co/projects/<project-uuid>/mcp
    headers:
      Authorization: "Bearer <your-token>"

Also works with Windsurf, Zed, Continue, and any other MCP-compatible client.

faq

Questions teams ask before connecting their systems.

Kognita is built for teams that need AI tools and people to share one grounded understanding of the codebase, connected data, and operational reality.

01

What is Kognita?

Kognita is a managed agent runtime built on a custom semantic engine. It connects your whole team — developers, product, support, QA, operations, and leadership — and the AI coding tools they use (Cursor, Claude Code, Codex, Windsurf) to your system through grounded answers. The result is shared AI adoption: one source of truth across the company, instead of every role and tool guessing on its own.

02

What is semantic code intelligence?

Semantic code intelligence means Kognita looks beyond raw files and keyword search. It identifies services, functions, routes, jobs, database touchpoints, dependencies, ownership, business intent, and cross-repository behavior, then serves that understanding as a shared layer for AI agents and teams.

03

How does Kognita help AI coding tools?

Kognita exposes your indexed codebase through MCP so tools like Claude Code, Cursor, Codex, and Windsurf can retrieve relevant system context before they plan or edit. That helps agents avoid guessing from a few open files or rediscovering the same architecture in every session.

04

Can non-technical teams use Kognita?

Yes. Product, support, QA, operations, and leadership can ask plain-language questions in the browser and get answers grounded in the same codebase and connected systems that engineering uses. They do not need a terminal, local clone, SQL knowledge, or a developer beside them.

05

How does Kognita connect management to systems?

Kognita gives managers and leaders a safe way to ask about implementation status, release risk, customer impact, operational behavior, ownership, and system dependencies without turning every question into an engineering investigation or custom report request.

06

Can Kognita connect to custom MCP servers like Zapier?

Yes. A Kognita project can connect to approved public HTTPS custom MCP servers using Streamable HTTP or SSE. That means the managed agent can use external MCP tools, including Zapier MCP app actions, alongside semantic codebase context, Jira, and database intelligence.

07

Does Kognita replace developers?

No. Kognita is designed to give developers and the people around them better context. Engineers still build and review the software; Kognita helps AI tools and teammates understand the system from evidence before decisions are made.

08

Which repositories and tools does Kognita support?

Kognita is built around Git repositories from GitHub, GitLab, and Bitbucket, and exposes context through chat and MCP-compatible AI coding tools including Claude Code, Cursor, Codex, and Windsurf.

get started

Turn your codebase into answers every team can use.

Connect a repo and Kognita builds a semantic map your developers, AI tools, and non-technical teammates can all query in plain language. Get started in minutes.

Try Kognita ↗See case studies