Financial data workflows —
from months of grind
to minutes of output.
Kamba is the operational layer that turns sourcing, validation, backtesting, procurement, and reporting into automated, auditable workflows — embedded where your team already works, without moving your data.
Your teams are brilliant. Your workflows are the bottleneck.
Finance data teams don't lack talent — they lack operational leverage. Sourcing the right dataset takes weeks. Validating quality is manual and inconsistent. Backtesting requires engineering queues. Procurement drags for months. Reporting is a spreadsheet grind with audit risk baked in.
Generic AI can answer questions about your data. Kamba does the actual work: it orchestrates the full procurement cycle between buyers and vendors, produces governed decision artifacts that compliance can defend in an audit, and compresses a 4-month sourcing-to-production pipeline into days. That's not a chatbot — it's an operational system for regulated data teams.
Kamba sits between buyers and vendors, orchestrating procurement end-to-end. No generic AI has vendor relationships, submission packs, or deal-flow orchestration.
Governed decision artifacts
Produces versioned, auditable DQRs, scorecards, and backtest reports — not chat transcripts. Evidence that compliance and risk teams can defend.
Runs on your Snowflake, S3, and vendor feeds. Surfaces inside Symphony or your own UI. No shadow copies. No new portal to enforce.
Estimate the annual drag from workflow friction
An illustrative planning model for CIO and data leadership. Quantify potential impact from reducing capacity waste, vendor waste, and decision latency.
See Kamba Analyst do the work
Three short clips. No promo fluff. Just the core workflows your teams care about.
Ask in plain language. Surface relevant internal and external datasets in seconds.
Open clip →
Coverage, gaps, anomalies, and stats in a structured view from a raw sample.
Open clip →
- One prompt scopes the request across internal and external data sources.
- Returns ranked dataset candidates and an auto-generated dataset brief.
- Standardized quality checks with issues logged and mitigations documented.
- Hard gate: failed datasets stop here — no wasted evaluation downstream.
- Continuous drift/decay monitoring once a dataset is approved.
- Explicit assumptions and reproducible methodology.
- Auto-codes, validates, and stress-tests strategies within firm-defined guardrails.
- Designed for IC, Risk, and Procurement review — not one-off notebooks.
- Approved, versioned artifacts with full lineage attached.
- Deep-dive domain reporting: investment, insurance & credit, risk, regulatory.
- Designed for reuse, monitoring, and refresh — not rebuild.
- All validated outputs stored, versioned, and governed — not scattered across notebooks.
- Approvals & permissions: role-based access control per team and room.
- Full lineage: every artifact traces back to source data and analyst.
- Audit trails: every decision logged — defensible months or years later.
- Reuse: approved evidence accelerates future decisions instead of rebuild.
Built for workflows that drive outcomes
Seven core workflows that compress the full data lifecycle — from thesis to governed decision artifact.
Centralize requests, samples, comparisons, and diligence so teams stop repeating work across disconnected tools.
Reduce friction from dataset to tested signal with repeatable checks, instant backtests, and explainable outputs.
Make outcomes reusable across teams with governed sharing, traceability, and versioned decision artifacts.
Because every dataset flows through one validated pipeline, Kamba also delivers: data-stack rationalization (redundancy detection, keep/fix/drop recommendations) · production monitoring (drift, breaks, latency, with alerts routed to the right owner) · institutional cataloging (auto-generated dataset briefs, decision logs, reusable evaluation artifacts) · vendor enablement (standardized onboarding, automated QA, buyer-persona matching) · enterprise integration (unified access across Snowflake, S3, vendor feeds, and docs). See the full use case catalog →
Not a chatbot. An analyst that does the work.
If someone could build this with a generic AI and some glue code, we wouldn't exist. Here's what they can't replicate.
Kamba manages the actual transaction — RFIs, submissions, diligence, policy gates — with both sides in sync. Generic AI answers questions. Kamba closes deals.
DQRs, vendor scorecards, backtest reports — versioned, governed, defensible in an audit. Not chat transcripts you have to screenshot and email.
Connects to Snowflake, S3, vendor feeds. Surfaces in Symphony. No shadow copies, no new portal, no re-platforming. Governed by design for regulated institutions.
Put Kamba Analyst in front of your data
If your teams are still stitching workflows together with email, tickets, and ad hoc notebooks — it's time to see Kamba on your stack.
- Heads of data and data strategy — compress delivery cycles and reduce vendor waste.
- Data sourcing and vendor teams — standardize evaluations and onboarding.
- PMs and quant leads — validate whether data and signals add P&L.
Place your Squarespace Form block here. Keep it short: name, firm, role, email, and one sentence on your workflow.