The question takes a minute. The work product used to take days.
Every workflow below runs in minutes. Every work product is sourced, auditable, and reproducible.
The work product starts the moment the thesis does — structured automatically with thesis, data foundation, signal testing, projections, and risk. Kamba sources and validates across internal systems, vendor feeds, and public sources, with full lineage from the first line.
- Consistent structure every time — regardless of analyst, deadline, or market conditions
- Every number defensible in committee and auditable months later
- Analyst time freed for judgment, not data assembly
A strategy idea becomes IC-ready work product in the time it used to take to pull the data — thesis, validation, forward projections, and risk scenarios built in, reusable at any cadence.
- From idea to documented, tested strategy in minutes
- Reproducible — run the same strategy next quarter with one prompt
- One standard across the desk, regardless of analyst
Coverage depth no longer depends on analyst availability. Structured financials, filings, transcripts, and estimates pulled in one pass — consistent work product across every name in the universe.
- Same depth on a name you've never covered as one you know well
- Filings, transcripts, and PDFs integrated automatically — no stitching
- Ready to present to a PM, IC, or client on arrival
Every approved thesis gets a live monitor from the moment it's confirmed. Confirmation levels, invalidation points, and close-by-close tracking built automatically — live data integrated, alerts routed to the right person the moment conditions are met or broken.
- Invalidation tracked alongside confirmation — know when the setup breaks before the position does
- Built in seconds for any thesis, any ticker, any market condition
- The monitor runs while the team focuses on decisions
IC prep, reporting, and distribution run in one governed workflow. The full submission — recommendation, data foundation, signal testing, risk scenarios, and exhibits — structured, version-controlled, and distributed with a complete audit trail across every submission, version, and recipient.
- Every submission version-controlled — know exactly what was presented and when
- Decisions defensible and traceable long after the meeting
- Analysts spend time on the recommendation, not the assembly
A thesis or research question becomes a ranked list of dataset candidates in one motion. Internal data lakes and external vendor feeds queried simultaneously — returning fit rationale, an auto-generated dataset brief, and source lineage for each candidate.
- Know what you have before the analysis starts — not halfway through
- Hours of vendor outreach and exploratory calls eliminated
- Auto-generated dataset brief for every candidate — ready for procurement
A vendor name, sample dataset, or data dictionary becomes a complete DQR in seconds — coverage, timeliness, gaps, anomalies, stability, and mapping readiness. Vendor scorecards and side-by-side comparisons generated with consistent methodology, ready for compliance sign-off.
- Repeatable evaluation — replaces inconsistent manual review
- Defensible vendor comparisons with consistent methodology
- Compliance-ready documentation generated automatically
Signal validation starts the day the idea is formed. Dataset-level backtests — signal extraction, validation logic, and performance attribution — run without custom engineering. Scheduled re-validation means the data team knows before the portfolio team finds out.
- Validate what a dataset is worth before it reaches any strategy
- Consistent methodology across competing vendor bake-offs
- Scheduled re-testing — decay flagged before it reaches live positions
Natural-language questions answered across structured and unstructured sources in one pass — warehouses, data lakes, PDFs, emails, and vendor feeds queried simultaneously. Every response includes lineage, assumptions, and computation steps. No digging. No conflicting answers.
- Lineage and assumptions visible for every work product
- Consistent metrics across the team — no more conflicting answers to the same question
- One interface — no data digging, no siloed workflows
Stack rationalization runs on evidence, not gut feel. Kamba takes a current data inventory, vendor contracts, or usage logs — detects redundancies, flags schema and methodology changes before they break downstream pipelines, and generates keep, fix, drop recommendations backed by usage, quality, cost, and overlap data.
- Know what you're paying for twice — redundancy surfaced automatically
- Change detection before it breaks your pipeline
- Systematic quarterly and annual reviews without the manual overhead
Every dataset gets a living brief automatically — what it is, why it's used, its limitations, and its lineage. Decision logs capture why datasets were bought, renewed, or cancelled and who approved. DQRs and evaluations stored as reusable work product, not one-off documents lost in email.
- Auto-generated documentation — not maintained manually
- Full history of every buy, renew, and cancel — with rationale and approvals
- New team members up to speed in hours, not weeks
Every data source — internal lakes, warehouses, and external vendor feeds — accessible through one interface without re-platforming. Unified permissions and traceability consistent across every connected source, whether the data came from Snowflake, S3, or a PDF.
- Internal and external in one interface — no switching, no stitching
- Permissions and traceability consistent across every source
- New sources connect without engineering tickets
Start with one workflow.
Expand from there.
You don't need to replace your stack. Start with the workflow that costs your team the most time — the rest follows.
Send us one workflow. We'll return a Kamba work product.
No demo environment. Your data, your question, a real work product.