Top Investigation How it works Platform Why dekx Outcomes Beyond the ticket Waitlist
SUPPORT COPILOT · MCP HUB · KNOWLEDGE GRAPH

The copilot that
investigates for you, with you.

not another chatbot, not another coding agent.

not a chatbot - doesn't just retrieve from docs, it investigates: pulling from source code, tickets, known issues, past resolutions and it's own crafted knowledge of your products.

not another coding agent - dekx investigates, reproduces, validates and responds.

it runs locally, so proprietary code and customer data never leave your infrastructure to get an answer.

Local harness
Local or remote models
17+ tools, one platform
No customer data leaves your infra
Chatbots

The answer has to already be written down somewhere.

Coding agents

Only see code, and only help in the IDE.

dekx
  • Finds the root cause.
  • Tests the answer.
  • Drafts a cited reply.
The investigation

One ticket in. A full investigation out.

Every step is a real tool call against your own systems — root-cause reasoning across code and documentation, not search and not autocomplete.

  1. 01
    Read the ticket
    #4821 · duplicate webhook deliveries
  2. 02
    Match the product profile
    Acme Cloud 4.2 · Events API
  3. 03
    Query the knowledge graph
    docs · KB · past resolutions
  4. 04
    Trace the source code
    dispatcher · retry policy
  5. 05
    Reproduce it
    generated sample, run locally
  6. 06
    Propose & validate fixes
    workaround, config change, upgrade path — each one tested
  7. 07
    Draft the response
    customer-ready, fully cited
dekx · local harness

Root cause & validated fix

Retries re-dispatch on a 504 from the subscriber without checking the delivery ledger, so a slow endpoint receives the same event twice. Fix: set ledger_check=true in the retry policy — validated against the repro — or upgrade to 4.2.6. Immediate workaround: de-duplicate on event_id.

docs/events/retries.mdKB-1180ticket #3902dispatcher.go:214release-notes 4.2.6
How it works

RAG retrieval. Code parser.

Semantic and fuzzy search of prose with a code parser.

Structural chunking

Docs, KB articles and tickets are split along their own structure — sections, steps, code blocks, threads — so a retrieved passage is a whole thought, not an arbitrary window of characters.

Code parsed as code

A tree-sitter pass reads source by syntax rather than by line: functions, types, config keys and call sites become addressable units, each traceable back to file and line.

Linked into a graph

Those units are connected — symptom to source, ticket to fix, doc to the version it describes. Retrieval follows the edges, which is what lets an answer explain itself.

Fed by your systems

Repos, issue trackers, doc sets and past resolutions connect through MCP and stay current, so the graph reflects the latest — not a snapshot.

The platform underneath

Indexed knowledge. Every system. Scoped per product.

Product profiles

Product identities

A profile per product — versions, components, repos, doc sets. Investigations get scoped to the customer's actual deployment, not your whole company.

# profiles/acme-cloud.yaml
product: Acme Cloud
versions: [4.0, 4.1, 4.2]
components: [events-api,
  dispatcher, webhooks-ui]
sources:
  docs:    ./docs/**/*.md
  code:    acme/events, ./sdk
  tickets: zendesk(brand=cloud)
  issues:  jira(EVT), linear
escalate_when: no repro × 2
Knowledge graph

Everything your products know, indexed in one place.

Built on your own machine — linking docs to code paths, tickets to resolutions, versions to known issues. Incremental, and nothing leaves your network to be embedded.

  • Documentation & guides md, sites, PDFs
  • KB articles Zendesk, Notion
  • Tickets & resolutions Zendesk, Salesforce
  • Source code & samples repos, GitHub
  • Issues & release notes Jira, Linear
  • Team decisions Slack threads
MCP hub

One MCP server for your support stack.

Connect once. Every system becomes tools your copilot — or any MCP-compatible client — can call, with scoped, read-first permissions you control.

Zendesk
Jira
Linear
Salesforce
Notion
Slack
GitHub
Confluence
Docs sites
Local repos
Local files
Your API
Why dekx

It works where others stall.

Support engineering isn't a search problem. It's a reasoning problem over your product, your code and every single data located in random places.

01Built around your products.
Product-centric

A profile per product pins every investigation to the customer's actual deployment — version, components, repos, doc set. General assistants answer about software; dekx answers about yours.

02Hunt for answers that were never written down.
Investigation, not retrieval

Retrieval can only return what someone already documented. dekx traces a symptom into the source, reads the code path, and reasons to a root cause — the tickets nobody can answer from the KB are the ones it's for.

03Nothing reaches a customer unproven.
Evidence by default

Every proposal comes with a reproduction it ran and a fix it validated, and every claim in the draft reply carries the file, line or article it came from. You review evidence instead of auditing prose.

04The harness runs on your system.
Local & private

On a support engineer's laptop or inside your VPC. Local offline model proprietary code and customer data stay where they are or a remote model with PII redactions for when you want more reasoning — the choice is yours.

Ollama · vLLM · any OpenAI-compatibleBYOM key, redaction enforcedIndex & logs on disk you ownNo vendor copy of your codebase
05Multiple tools behind one conversation.
One surface

Your platforms connect once through the MCP hub and stay current. Work in the dekx interface, the tools get called for you.

Outcomes

Faster investigations. Fewer escalations. Quicker resolutions.
Happier customers.

dekx doesn't replace your support engineers, it empowers them.
The judgment, the trust, the call on what a customer actually needs — that stays human.
What dekx removes is everything standing between your engineers and the results.

Knowledge scattered across six systems
→
One indexed graph every engineer queries
6 tabs, 3 apps per ticket
→
One surface, tools called for you
Escalations because there's no reproduction
→
Repro, workaround and sample code in hand
New hires learning the product for months
→
Day one with the team's whole history
Answers written from memory, relevance assumed
→
Every claim cited and checkable
Tier-1 and Tier-2 repetition eats the day
→
The repeatable volume handled, hours left for the tickets that need the time.
Experience locked in your most senior engineers
→
The biggest lift lands on the newest. +34% in a 5,000-agent study Read the article →
BEYOND THE TICKET

But there's more....

Investigating a specific case reads the same code and docs your team already shipped. What it finds along the way doesn't stay in the investigation run.

Bugs revealed

Tracing a symptom to its line of source surfaces the defect behind it, before another customer files it. Every finding and it's fix arrives with a reproduction attached.

Loopholes in the docs

When the answer isn't in the documentation, and isn't a bug in the code, dekx says so. The gap, the wrong assumption, the step that only works one way. It provides you a running list of what to fix or what you can do.

Version drift

Customers on older releases hit behaviours current docs no longer describe. dekx pins the answer to the version in front of it and flags where the two have diverged.

Early access

Put dekx in the queue
with your team.