Apply for jobs without retyping your life.
Your agent fills the salary expectation, the years of TypeScript, the projects that actually match the role. From your real work, not from a stale resume.
Example graph: one person connected to projects, source notes, claims, and evidence boundaries. Hover to inspect a record.
Your work. Your voice. Your evidence.
Marrow is a private, source-backed knowledge graph about your professional work. AI tools that draft, summarize, or speak for you can pull from it instead of guessing.
What your agents can do with Marrow.
AI is being asked to write, post, apply, and answer for you more and more. Marrow is what it pulls from when it needs your real work, in your real voice, instead of guessing.
Post on LinkedIn or X in your voice.
Marrow knows how you write and what you actually shipped. Your agent drafts; you edit once. Sounds like you, because it is.
Answer "show me experience with enterprise onboarding" without scrambling.
A prospect asks. Your agent pulls the right projects, scale, and outcomes. Concrete enough to land, cited so they can verify.
Keep your portfolio current without rewriting it.
New project shipped? Add one source note. Your bio, profile, and portfolio update on their own. Evidence stays attached.
Run an agent that actually knows you.
"Have you led a team of eight? Shipped on a two-week deadline?" Your agent answers from your real work, and tells you when it doesn't know instead of guessing on your behalf.
Pitch yourself with the specifics, not the cliché.
Investor or founder reaches out. The pitch lands because it's drawn from what you actually did, not a generic one-liner an LLM spun up about you.
Behind every use case is a typed, source-backed record:
Example record
One useful fact, with the evidence attached.
- Claim
- Avery Quinn led the Northstar rollout, Jan to Apr 2026.
- Source
- Example project notes, dated 12 May 2026
- Confidence
- Directly stated in the source
- Visibility
- Public summary allowed
- Supersedes
- None
Ask the example corpus.
Avery Quinn is a fictional example person. Pick a question and see what an application can retrieve from Avery's source-backed corpus before it writes, answers, or accepts a correction. Local demo data, not a live hosted account.
Use the corpus before your product writes.
The non-technical flow a product should expose. Sources go in, evidence comes out, corrections are reviewed, and every step stays inspectable.
Upload or connect sources
Add a CV, project note, writing sample, public profile, case study, repository note, or publication. Each source keeps a date and visibility setting.
Ask a grounded question
Ask what the work supports before a product writes a bio, proposal, summary, recommendation, or agent answer.
Inspect what came back
Review the answer, the source labels, the confidence, and any boundary that says what the product should avoid claiming.
Source in
A user adds Avery's example profile notes and marks them as usable for a public summary.
Expected result: Marrow stores the source and prepares it for retrieval.
Uploaded source
Example profile notes
Dated 12 May 2026 · Public summary allowed
Question asked
The product asks Marrow before writing about Avery's project management experience.
Expected result: Marrow returns the strongest answer it can support from the source material.
Question
What evidence says Avery has experience in project management?
Evidence shown
The product can show the source-backed answer instead of asking the user to trust a generated paragraph.
Expected result: The product receives evidence about the Northstar onboarding rollout, launch plan, risk reviews, and stakeholder updates.
Retrieved answer
Avery led the Northstar onboarding rollout.
Confidence: directly stated · Source: Example profile notes
Correction approved
If a product overstates the role, the user can approve a narrower correction that future reads should prefer.
Expected result: Marrow keeps the older claim inspectable and links it to the safer replacement.
Boundary
Avery coordinated with engineering but did not line-manage engineering teams.
Future drafts should avoid the engineering-manager claim.
What your product can show
Answer
A short result the user can understand before a draft is written.
Evidence
Source labels, excerpts, confidence, and visibility.
Boundary
What the source does not support and should not be claimed.
Correction
A reviewed replacement that future products can reuse.
Building on Marrow? See the developer docs for the API, CLI, and integration patterns.
Where Marrow creates the most value.
The strongest fit is professional identity that is source-rich, nuanced, and trust-sensitive. Generic AI tends to flatten exactly this kind of work.
Source-rich work
Projects, writing, research, founder material, public talks, publications, repositories, or case notes.
Nuanced representation
Work that generic AI tends to flatten, overstate, or describe without the right caveats.
Trust-sensitive output
Profiles, bios, proposals, applications, or agent answers where evidence and restraint matter.
Private records can still be cited.
Marrow distinguishes public evidence, private evidence, inferred evidence, and directly confirmed evidence.
The person controls what gets exposed, to whom, and why. Feedback enters as source-backed records, corrections, or superseded claims that can be inspected.
Future attestations should attach to specific claims, not generic reputation. They remain a trust roadmap primitive, not a public lookup product.
Accuracy
Important claims are correct, scoped, and backed by source records.
Specificity
The output preserves the work's concrete projects, artifacts, judgment, and outcomes.
Voice
The writing uses style evidence without inventing new facts.
Corrections
Accepted edits become durable source-backed updates instead of hidden prompt memory.
For people whose work needs more context than a profile page can hold.
Marrow starts with source material, writing samples, project history, public evidence, and corrections that should stay available to the applications a person uses.
Get started