Note on Examples
The examples in this article are hypothetical but realistic scenarios designed to illustrate how AutoDoc works across different industries. They are based on common R&D patterns and typical documentation challenges that enterprise companies face.
AutoDoc automatically captures, structures, and links R&D evidence from your existing tools to support tax credit claims. This article explains what AutoDoc does, why it matters, and how it works through detailed examples across software, manufacturing, life sciences, and other R&D-intensive industries.
Each example shows the complete flow: from R&D work happening in your tools, to evidence being captured automatically, to claims being supported with traceable documentation. These examples are designed to be clear, practical, and immediately applicable to your situation.
What AutoDoc Does
AutoDoc is an evidence capture and traceability system for R&D tax credit claims. It does three things:
Captures Evidence Automatically
Integrates with GitHub, Jira, Confluence, SharePoint, and other tools to automatically ingest code commits, tickets, documentation, and other evidence as R&D work happens.
Links Evidence to Claims
Uses AI to suggest links between evidence and claim assertions, with human review gates ensuring accuracy. Creates complete traceability matrices.
Generates Audit Packs
Assembles complete audit packs with evidence index, traceability matrices, review history, and all supporting documentation for claim submission or audit defense.
Key Capabilities
- Automatic evidence ingestion from 15+ source systems
- Immutable evidence storage with complete provenance
- AI-assisted traceability linking with human review gates
- Complete audit trail logging
- Audit pack generation on demand
- Works alongside existing workflows (no disruption)
- Supports both SR&ED (Canada) and US R&D tax credit workflows
Why AutoDoc Matters
Most companies struggle with R&D tax credit documentation because they:
- Gather documentation retroactively after R&D work is complete
- Rely on spreadsheets and ad-hoc document collections
- Cannot trace claims to specific evidence items
- Lose evidence when team members leave or systems change
- Struggle to prove contemporaneous documentation during audits
AutoDoc solves these problems by:
- Capturing evidence automatically as R&D work happens, ensuring contemporaneous documentation
- Storing evidence immutably with complete provenance, so it remains defensible years later
- Creating traceability links between claims and evidence, enabling complete audit defense
- Maintaining complete audit trails of all actions, building trust with tax authorities
- Generating audit packs on demand, even years after claims are filed
The result: R&D tax credit documentation that is defensible, traceable, and audit-ready from day one.
How AutoDoc Works: Software Development Example
Hypothetical Example: TechFlow Inc., a software company developing a new machine learning recommendation engine.
The R&D Work
TechFlow's engineering team is developing a new recommendation algorithm that must handle real-time updates and cold-start problems (recommending items to new users with no history). This involves:
- Researching existing recommendation algorithms and their limitations
- Designing a hybrid approach combining collaborative filtering with content-based methods
- Implementing and testing the algorithm with real user data
- Optimizing performance to meet real-time latency requirements
What AutoDoc Captures
As the engineering team works, AutoDoc automatically captures evidence from their tools:
From GitHub:
AutoDoc captures: commit hash, author, timestamp, commit message, files changed, and code diff.
From Jira:
AutoDoc captures: ticket ID, title, description, creation date, assignee, status, comments, and technical details.
From Confluence:
AutoDoc captures: page title, content, creation date, author, revisions, and attachments.
How AutoDoc Structures the Evidence
AutoDoc normalizes all captured evidence into a common structure with complete provenance:
How AutoDoc Links Evidence to Claims
When TechFlow's tax advisor creates a claim assertion, AutoDoc suggests evidence links:
Claim Assertion:
"Developed novel hybrid recommendation algorithm combining collaborative filtering with content-based methods to solve cold-start problem for new users, addressing technical uncertainty in real-time recommendation systems at scale."
AutoDoc Suggested Links:
Implements the hybrid algorithm described in assertion
Confidence: 95% | Requires Review: Yes
Documents the cold-start problem and technical uncertainty
Confidence: 90% | Requires Review: Yes
Provides design rationale and algorithm selection reasoning
Confidence: 85% | Requires Review: Yes
The tax advisor reviews each suggested link, approves the strong ones, and the system creates a traceability matrix showing how the claim assertion is supported by evidence.
How AutoDoc Works: Manufacturing Example
Hypothetical Example: Precision Manufacturing Co., developing a new composite material for aerospace applications.
The R&D Work
Precision Manufacturing's engineering team is developing a new carbon fiber composite that must meet strict weight, strength, and temperature resistance requirements for aircraft components. This involves:
- Researching composite formulations and fiber orientations
- Testing material properties under various conditions
- Optimizing manufacturing processes to achieve target specifications
- Validating performance through prototype testing
What AutoDoc Captures
From SharePoint (Test Results):
AutoDoc captures: document metadata, content, test data, creation date, author, and technical context.
From Jira (Process Development):
AutoDoc captures: process development activities, experimental iterations, and technical problem-solving.
From Email (Research Notes):
AutoDoc captures: research activities, literature review, and technical analysis.
How AutoDoc Links Evidence to Claims
AutoDoc links manufacturing evidence to claim assertions:
Claim Assertion:
"Developed novel carbon fiber composite material formulation and manufacturing process to achieve simultaneous weight reduction, strength improvement, and temperature resistance for aerospace applications, addressing technical uncertainty in composite material design."
Evidence Links:
How AutoDoc Works: Life Sciences Example
Hypothetical Example: BioInnovate Labs, developing a new drug delivery system for targeted cancer therapy.
The R&D Work
BioInnovate's research team is developing a nanoparticle-based drug delivery system that targets cancer cells while minimizing side effects. This involves:
- Designing nanoparticle formulations with specific surface properties
- Testing drug loading and release kinetics
- Validating targeting specificity in cell cultures
- Optimizing stability and biocompatibility
What AutoDoc Captures
From Lab Notebook System:
AutoDoc captures: experimental protocols, results, dates, researchers, and technical challenges.
From Research Management System:
AutoDoc captures: research plans, technical uncertainties, and project documentation.
How AutoDoc Links Evidence to Claims
AutoDoc links life sciences evidence to claim assertions:
Claim Assertion:
"Developed novel nanoparticle-based drug delivery system with modified surface ligands to achieve improved targeting specificity for cancer cells, addressing technical uncertainty in targeted drug delivery for this drug class."
Evidence Links:
Common Patterns Across Industries
While the specific tools and evidence types vary by industry, AutoDoc follows the same pattern:
R&D Work Happens
Engineers, researchers, or developers work on qualifying R&D activities using their normal tools (GitHub, Jira, lab notebooks, etc.).
AutoDoc Captures Evidence
AutoDoc automatically ingests evidence from source systems with complete provenance (source, creator, timestamp).
Evidence is Stored Immutably
Evidence is stored with cryptographic hashing and versioning, ensuring it remains defensible years later.
AI Suggests Links
When claim assertions are created, AutoDoc's AI analyzes evidence and suggests links with confidence scores.
Humans Review and Approve
Tax advisors or qualified reviewers review suggested links, approve strong ones, and reject weak ones.
Traceability Matrix Created
The system creates a complete traceability matrix showing how each claim assertion is supported by evidence.
Audit Pack Generated
When needed (for submission or audit), AutoDoc generates a complete audit pack with all evidence and traceability.
See AutoDoc in Action
AutoDoc automatically captures, structures, and links R&D evidence from your existing tools. See how it works for your industry and discuss with your team.
AutoDoc integrates with GitHub, Jira, Confluence, SharePoint, and 15+ other tools. Setup takes minutes, and it works alongside your existing workflows.
Frequently Asked Questions
How does AutoDoc capture R&D evidence automatically?
AutoDoc integrates with your existing tools (GitHub, Jira, Confluence, SharePoint, etc.) and automatically ingests code commits, tickets, documentation, and other evidence as R&D work happens. It captures evidence with complete provenance (source, creator, timestamp) and stores it immutably for future audit defense.
What types of R&D evidence does AutoDoc capture?
AutoDoc captures code commits, pull requests, Jira tickets, Confluence pages, design documents, test results, meeting notes, technical specifications, research logs, experimental data, and any other contemporaneous documentation created during R&D work. It works across software development, manufacturing, life sciences, and other R&D-intensive industries.
How does AutoDoc link evidence to R&D tax credit claims?
AutoDoc uses AI to analyze evidence and suggest links to claim assertions, but all links require human review and approval. The system creates traceability matrices showing how each claim assertion is supported by specific evidence items, with relationship types (direct support, context, background) and confidence levels.
Does AutoDoc work for software companies?
Yes. AutoDoc is particularly effective for software companies because it automatically captures code commits, pull requests, technical discussions, and design documents from GitHub, GitLab, Jira, and other development tools. It identifies qualifying R&D activities like algorithm development, architecture improvements, and technical problem-solving.
Does AutoDoc work for manufacturing companies?
Yes. AutoDoc captures process improvement documentation, material testing results, design iterations, quality control experiments, and engineering notes from manufacturing systems. It helps identify qualifying R&D activities like developing new manufacturing processes, improving product performance, and solving technical uncertainties.
Does AutoDoc work for life sciences companies?
Yes. AutoDoc captures research protocols, experimental results, lab notes, clinical trial documentation, and regulatory submissions from life sciences systems. It helps identify qualifying R&D activities like drug development, medical device innovation, and biological research with proper scientific documentation.
How does AutoDoc ensure evidence is contemporaneous?
AutoDoc captures evidence as R&D work happens, preserving original timestamps from source systems (GitHub commits, Jira ticket creation dates, etc.). The system maintains complete provenance showing when evidence was created, ensuring it is contemporaneous and defensible during audits.
Can AutoDoc generate audit packs for R&D tax credit claims?
Yes. AutoDoc generates complete audit packs including evidence index, traceability matrices, review history, and all supporting documentation. These audit packs can be generated years later for audit defense, with complete provenance and review history preserved.
How long does it take to set up AutoDoc?
AutoDoc can be set up in minutes. You connect your existing tools (GitHub, Jira, etc.) via OAuth, and AutoDoc begins ingesting evidence automatically. The system works alongside your existing workflows without disruption, capturing evidence as R&D work happens.
Does AutoDoc require changes to our development workflow?
No. AutoDoc integrates with your existing tools and workflows without requiring any changes. It captures evidence automatically as your team works, so engineers continue using GitHub, Jira, and other tools exactly as they do today. No additional documentation steps are required.