AI coding session record and search console

For

Engineering teams and individual developers using AI coding agents

Solves

AI coding sessions are not recorded, searchable or inspectable, so debugging and collaboration rely on memory and manual notes.

Delivers

Searchable, reviewable session record

Built in

about 5 weeks of creation time, MVP in 6 days

Investment

$14,500 for the MVP, $49,500 for the full product

Run it

Inside your business, or as part of your offer to clients

Reduce debugging time and improve team collaboration by making AI coding sessions searchable and inspectable.

1. Record AI coding sessions automatically.
2. Expose tool calls, file changes and conversation context.
3. Generate shareable session links and PR embeds.
4. Provide privacy controls for deletion, expiration and opt-in sharing.
5. Summarize sessions for quick analysis.
6. Display tool calls, approvals and threads in columns.
7. Allow branching conversation threads for experiments.
8. Store sessions persistently across crashes and reconnects.
9. Support multiple live client views and headless operation.
10. Make every tool call a pluggable, inspectable JavaScript plugin.
11. Index historical conversations at install time.
12. Run entirely locally with no account or upload.
13. Encrypt the index with SQLCipher and macOS Keychain.
14. Re-run indexing on parse failure.
15. Search across all indexed tools from one bar.
16. Reconstruct execution traces from local logs.
17. Visualize token and context consumption per turn.
18. Show inline diffs and syntax-highlighted file reads.
19. Display subagent execution trees and tool call chains.
20. Index sessions per project via git root.
21. Search semantically using local embeddings.
22. Fall back to keyword search when embeddings are unavailable.
23. Inject relevant past context into new sessions.
24. Provide CLI commands for mining, search, embed and stats.

Everything these tools do, in one app

• Automatic session recording Automatically records AI coding sessions without manual steps.Found in Bench for Claude Code
• Detailed action traces Exposes tool calls, file changes, and conversation context to see what led to each change.Found in Bench for Claude Code, claude-devtools
• Shareable session links Generates a single link to share an entire session or embed history in pull requests.Found in Bench for Claude Code
• Privacy controls Allows deleting traces, setting expirations, using separate tracking codes, and opt-in sharing.Found in Bench for Claude Code
• Session summaries Provides summary helpers to speed up session analysis.Found in Bench for Claude Code
• Column-based layout Displays tool calls, approvals, thread structure, and raw context in adjacent columns.Found in Juggler
• Branching conversation threads Allows forking a session into sub-threads to isolate experiments and backtrack without losing context.Found in Juggler
• Persistent sessions Stores sessions in a database so they survive quits, crashes, and reconnects.Found in Juggler
• Multi-client operation Supports multiple live client views simultaneously, including headless operation.Found in Juggler
• Pluggable tools Allows every tool the model calls to be a JavaScript plugin that can be inspected, forked, or replaced.Found in Juggler
• Historical indexing Indexes all historical conversations from supported tools at install time, not just new ones.Found in Inventory
• Local-only storage Runs entirely on the local machine with no account creation or data upload.Found in Inventory, session-indexer, claude-devtools
• Encryption Encrypts the index file with SQLCipher, storing the key in macOS Keychain.Found in Inventory
• Self-repair mechanism Re-runs indexing on next launch if a parse fails, rather than recording a failed parse as completed.Found in Inventory
• Unified search bar Searches across all indexed tools simultaneously from a single bar.Found in Inventory
• Execution trace reconstruction Reconstructs full execution traces from local session logs without modifying the CLI.Found in claude-devtools
• Token and context visualization Shows per-turn context attribution and token consumption breakdowns, including compaction visualization.Found in claude-devtools
• Inline diffs and file reads Displays inline diffs for file edits and syntax-highlighted file reads.Found in claude-devtools
• Subagent execution trees Shows subagent execution trees and tool call chains with inputs and results.Found in claude-devtools
• Per-project indexing Indexes sessions per project, resolving the database path via git rev-parse --show-toplevel.Found in session-indexer
• Semantic search Searches session history using bge-m3 embeddings via Ollama.Found in session-indexer
• Keyword search fallback Automatically falls back to FTS5 BM25 keyword search when Ollama is not running.Found in session-indexer
• Context auto-injection Injects relevant past context at the start of a new session.Found in session-indexer
• CLI commands Provides commands like mine, search, embed, and stats, with a --db flag for custom database paths.Found in session-indexer

What goes in, what comes out

The workflow

1. In
   Start with

   Local session logs, tool calls, file changes and conversation context

2. 1

   Confirm the buyer's problem and scope

3. 2

   Collect local session logs

4. 3

   Tool calls

5. 4

   File changes and conversation context

6. 5

   Then follow this sequence: 1

7. Out
   Finish with

   Searchable, reviewable session record

AI does the heavy lifting, people stay in charge

Use AI to interpret permitted inputs, suggest structured mappings and generate candidate outputs for the three stated task modules. Use deterministic code for arithmetic, schema validation, hard constraints and reproducible tests. Review source-linked explanations and uncertainty before accepting results. Local-only storage and encryption; final debugging and collaboration decisions remain with the developer. A model suggestion is never a verified fact, professional decision or authorization to act.

What your team sees

Primary screens: Session library, Session detail, Search and filters. Use a list or table for sessions with columns for tool, project, date and summary. Provide a detail view with columns for tool calls, approvals, thread structure and raw context. Include a unified search bar with semantic and keyword modes. Show privacy controls and expiration settings. Display session state as recorded, indexed, shared or expired. Make the task-specific outcome searchable, reviewable session record visible beside its evidence, review state and value baseline.

Accounts and administration

Project ownership, session versions, share links, expiration states, usage allowances, search history and a rights record for supplied material. Add organization access boundaries, named reviewers, usage caps, data retention controls, export logs and explicit approval for external actions.

Integrations and data access

Developer-owned session logs, authorized tool outputs and permitted research sources. Local file storage, git repositories and CI destinations. Start with file exchange and validate destination specifications before promising direct publishing. Start with authorized file exchange. Validate current provider access, usage rights and schema behavior before promising a connector.

We build with our own AI software development factory, so most implementations take days to a few weeks of creation time, not months. You see working software at every step, and exact timing depends on availability.

1. 1

   Scoping call

   Day 1

   Thirty minutes on your process, your data and how you want to run it: for your own team, or for your clients. You get a fixed scope and price for the MVP.

2. 2

   MVP

   6 days

   One buyer segment, one recurring use case; first modules: record AI coding sessions automatically; expose tool calls, file changes and conversation context. Manual review in the loop. Built by our AI software factory.

3. 3

   Paid pilot

   7 days

   Accounts, roles, review states, audit trail and the first integration, hardened for two to three paying pilot customers.

4. 4

   Full product

   2 weeks

   Self-serve onboarding, billing, monitoring and the wider integration set.

5. 5

   Run and improve

   Monthly

   We host, monitor and improve it for a fixed monthly fee, or hand it over to your team. How the retainer works.

Why we start with an MVP

An MVP, or minimum viable product, is the smallest version that your users can actually work with. It is not a cheap version of the full solution. It is a test, built to answer the questions that decide whether the rest is worth building.

1. Pick the riskiest assumption. Here: will engineering teams and individual developers using AI coding agents use it to solve "AI coding sessions are not recorded, searchable or inspectable, so debugging and collaboration rely on memory and manual notes"?
2. Build only what tests it. One team, one use case, a few core modules. People do the rest by hand for now.
3. Run a paid pilot. Agree quality and outcome thresholds before the pilot using this measure: Time to find a past session, debugging time saved and team collaboration quality.
4. Measure, then decide. Track time to find a past session and debugging time saved and team collaboration quality; accepted-output rate; material error rate; reviewer correction time; actual repeat purchase. Then expand, change course or stop, with evidence instead of opinions.

MVP scope for this solution. Pilot scope: Local-only storage and encryption; final debugging and collaboration decisions remain with the developer. Implement one approved input format, a bounded representative case set and the first two task modules: record AI coding sessions automatically; expose tool calls, file changes and conversation context. Support the third module with operator review: generate shareable session links and PR embeds. Include source references, corrections, basic organization access, approval states, export and value measurement. Use managed operator assistance for unresolved exceptions. The cost estimate covers this narrow prototype, not unrestricted multi-tenant scale, complex production integrations, specialist certification or physical operations.

After the MVP. Once paid pilots prove usefulness, automate repeatable reviewed steps and add one verified source integration. Expand supported inputs and case volume only after new evaluation cases pass. Build reusable customer configurations and recurring value reports around searchable, reviewable session record. Retain the explicit scope boundary: Local-only storage and encryption; final debugging and collaboration decisions remain with the developer.

What the build depends on. Session upload and preview, asynchronous indexing jobs, editable version history, reviewer access and tested export formats. High-fidelity production requires specialist development QA. Obtain representative authorized cases, baseline measurements, qualified reviewers and a buyer-side decision owner. Specific limitation: Local-only storage and encryption; final debugging and collaboration decisions remain with the developer.

A planning range to start the conversation, not a quote. You pay per phase, so you can stop after the MVP.

Running costs per month

A rough indication of monthly hosting and AI model costs once it is live, not tested. Real costs depend on usage, file sizes and the models chosen.

Your brand, or this one

Run it under your own brand, or start from this concept style.

Selling it to your own clients: the go-to-market playbook

Pricing to test

Test a USD 300-1,500 fixed pilot for one defined session package. Offer a monthly production allowance after repeat demand. Quote complex integrations or specialist support separately. These are test prices, not market benchmarks. Package the initial sale as one bounded searchable, reviewable session record. Recurring fees must specify volume, review depth and integration support. For exchanges, test a disclosed coordination or successful-service fee rather than holding customer funds. Reprice only after measuring real delivery labor; platform-build cost is separate from a commercial pilot fee.

Message to test

Reduce debugging time and improve team collaboration by making AI coding sessions searchable and inspectable. Demonstrate a concrete searchable, reviewable session record using the buyer's approved example and show the baseline, corrections and actual delivery effort.

Where to find buyers

Engineering teams and individual developers using AI coding agents professional communities; specialist consultants serving this buyer; permissioned partner introductions; practical demonstrations at relevant trade or practitioner events.

Lead magnet

A reviewed sample searchable, reviewable session record from a small authorized input set, with a transparent calculation of time to find a past session, debugging time saved and team collaboration quality and no promised savings.

The first 30 days

1. Week 1: interview five engineering teams and individual developers using AI coding agents and inspect a recent example of AI coding sessions are not recorded, searchable or inspectable, so debugging and collaboration rely on memory and manual notes.
2. Week 2: prepare a consented or synthetic demonstration of the three task modules.
3. Week 3: seek one bounded paid pilot with agreed baseline and acceptance criteria.
4. Week 4: measure time to find a past session, debugging time saved and team collaboration quality, reviewer effort and repeat-purchase interest. This is a demand-validation plan, not a thirty-day full-product delivery promise.

Paid pilot

Agree quality and outcome thresholds before the pilot using this measure: Time to find a past session, debugging time saved and team collaboration quality. Continue only if the buyer accepts the actual output, the intended job outcome improves without unacceptable errors, and measured delivery cost fits willingness to pay. Revise or stop if access is unavailable, qualified review cannot be provided, or apparent savings disappear after corrections and support. Use held-out cases when comparing model quality; use a properly reviewed comparison design before making causal claims. Record missing cases and negative results alongside successful outputs.

Success metrics

Time to find a past session, debugging time saved and team collaboration quality; accepted-output rate; material error rate; reviewer correction time; actual repeat purchase.

Retention and expansion

Repeat the workflow when the buyer again needs searchable, reviewable session record. Retain permissioned settings and reviewed examples, report realized value honestly, and sell increased volume or adjacent approved workflows only after contribution margin and quality remain acceptable.

Why clients would pick it

A reusable library of approved session formats, search indexes and review examples, together with reliable delivery for a narrow development niche. Build a permissioned library of representative task cases, reviewer corrections and verified operating constraints for engineering teams and individual developers using AI coding agents. Repeatable delivery and useful integrations matter more than access to a base model.

Alternatives and positioning

Bench for Claude Code, Juggler, Inventory, claude-devtools, session-indexer. Compare this product with the buyer's present method on time to find a past session, debugging time saved and team collaboration quality. Offer a bounded paid workflow instead of claiming broad autonomous expertise. Market uniqueness and competitor coverage are not verified.

Main delivery costs

Storage, indexing compute, reviewer hours, client revision rounds and licensed source assets. Additional initial validation requires representative authorized sample preparation, buyer interviews, buyer-side evaluation and bounded validation of searchable, reviewable session record. Track cost per accepted output, including correction work, unsuccessful cases and support.

Preserve developer voice, source attribution, quotation accuracy and usage permissions. Developers approve substantive changes and publication scope. Local-only storage and encryption; final debugging and collaboration decisions remain with the developer. Keep all consequential actions under authorized human control and do not fabricate missing inputs, permissions, professional judgments or market evidence.