Decispher - The Flight Recorder for AI Coding | System of Record for Engineering Decisions
The flight recorder for AI coding<br>AI agents don't fail because they're stupid.<br>They fail because they're new.<br>Every confident, wrong line of AI code costs you tokens, review cycles, and trust. Decispher captures the decisions, conventions, and constraints from the conversations your team is already having in Slack, GitHub, and Jira, then serves them back to every human and every agent that needs them. Automatically.<br>Get early access<br>Private beta · invite-only·SOC 2 in progress·No credit card
Humans<br>Dashboard · Slack · Jira · IDE
AI agents<br>Claude · Cursor · Copilot · MCP
NEWChat Clipboard · transfer mid-task context across sessions, machines & AI toolsNEWBranch Story · every branch has a story, never start a session cold again
The problem<br>Your team has the answers.<br>Your new hires and AI agents don't.<br>Every engineering team has two kinds of knowledge. The explicit kind lives in GitHub: code, READMEs, tickets. The tacit kind lives in people's heads: why the architecture is that way, what was tried and failed, which invariant must never break. AI agents only see the first one.
PROBLEM · 01<br>Confidently wrong code<br>Your AI agent re-introduces a bug your team fixed nine months ago. The convention it violated was decided in a Slack thread and never written down. The fix becomes another invisible constraint the next model will also miss.<br>cost · review cycles + tech debt
PROBLEM · 02<br>Token waste<br>Every agent call dumps 8,000 tokens of guessed-at context because nobody curates it. You pay Claude to rediscover what your team already figured out. The bigger your codebase, the worse the bill and the slower every call.<br>cost · ~40% of agent budget, every call
PROBLEM · 03<br>Onboarding decay<br>Every new engineer spends three months reverse-engineering institutional history from Git blame and lunch conversations. Knowledge that should be a one-query answer turns into a months-long apprenticeship.<br>cost · 90 days to productive output
PROBLEM · 04<br>Context amnesia between sessions<br>Your AI agent rebuilds the same mental model every session. Yesterday's reasoning, the approach it already abandoned, the constraint it learned the hard way: all gone when the session ends. The next session starts from zero and repeats the mistakes.<br>cost · repeated work, repeated mistakes
What Decispher is<br>Three jobs. One system of record.<br>Decispher reads the conversations your team already has, structures the durable signal into seven canonical context units, and serves them back to every consumer that needs them: humans in their IDE and dashboard, AI agents over MCP.
01 · CaptureFrom the work, not on top of it.<br>Bots listen in Slack channels. Webhooks watch GitHub PRs and reviews. The Jira app reads ticket threads and feeds context back when an agent picks the ticket up. No new tool for engineers, no manual documentation step. Decisions are extracted from the conversations, tickets, and PRs your team is already having.
02 · FuseThe same decision, said three times, becomes one.<br>A five-step LLM pipeline classifies every message, extracts the structured why, and fuses fragments across channels. Same idea in Slack and PR description? Merged. Multi-source agreement boosts confidence. Conflicts are flagged.
03 · Serve & enforceEvery consumer, the right amount, on demand.<br>Humans get search, “Ask Decispher,” and PR comments. Agents get MCP tools and 9 native instruction files. And when code contradicts a decision on record, the PR gets flagged before a reviewer ever opens the diff.
Feature 01 · Capture<br>Read where the work happens. Change nothing.<br>Ninety percent of engineering decisions are made in conversation and never documented. If you can't capture there, you can't capture at all. Decispher plugs in as a Slack bot, a GitHub webhook, and a Jira app. Your team writes the same messages they always have; we do the rest.
Sources · 3 live · 3 in pilot<br>SlackLive<br>GitHubLive<br>JiraLive<br>TeamsSoon<br>LinearSoon<br>NotionSoon
Pipeline · five steps · the same engine for every source<br>01 · Detect→02 · Extract→03 · Enrich→04 · Format→05 · Dedup<br>Output · seven canonical context-unit types<br>DECISION"chose React over Vue"<br>CONVENTION"all API routes versioned"<br>CONSTRAINT"never store JWT in localStorage"<br>RATIONALE"BullMQ — no cold starts"<br>HISTORY"Prisma tried '22, failed"<br>OWNERSHIP"@auth-team owns JWT"<br>PLAN"monorepo before Q4"
Feature 02 · Context Fusion<br>The same decision, said three times,<br>becomes one record.<br>A constraint mentioned in Slack, repeated in a PR description, and confirmed in a design doc should not become three rules. Decispher's Fusion engine embeds every fragment, finds the ones that mean the same thing, and merges them into a single, multi-sourced unit. Cross-channel agreement raises confidence. Disagreement raises a flag.
RELATIONSHIP TYPES<br>EXTENDS · CONFIRMS · CONTRADICTS · SUPERSEDES · SAME_TOPIC · UNRELATED
0.95<br>DEDUP THRESHOLD<br>Cosine similarity over 1536-d...