In production at Fortune 500 firms

Ship AI agents your enterprise can trust.

Turn any workflow into a governed AI agent, in days, with the controls your enterprise already requires.

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app.dataworkz.com/agents/templates
alex.morgan AM
18 Templates found
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All18 Financial Services6 Life Sciences4 Manufacturing4 Cross-vertical4
Regulatory Lineage Agent
FS

Extracts column-level lineage from Pentaho, SQL, and COBOL pipelines. Publishes Collibra-compatible outputs.

Call Deflection Agent
FS

Builds Spark jobs from DB2 CDC data; loads results through the client portal API. Deflects high-volume client calls.

Invoice Validation Agent
FS

Verifies invoices: extracts fields, matches to contracts, checks budget and hours, routes exceptions for approval.

Financial Advisor Assistant
FS

Surfaces account context, prior interactions, and next-best-actions for advisors during client conversations.

Travel Claims Processor
FS

Assists with processing travel insurance claims: submission, validation, and status updates.

KYC Document Review
FS

Reads onboarding docs against KYC rule sets; routes ambiguous cases and produces an audit trail.

Engineering Change Agent
LS

Reviews engineering change requests against quality records. Routes exceptions to a human approver.

Submission Lineage Agent
LS

Traces lineage of regulatory submissions across CMC, clinical, and quality systems for audit readiness.

Customs HTS Classifier
MFG

Classifies imports against the Harmonized Tariff Schedule. Suggests duty code, flags ambiguous cases.

ERP Exception Handler
MFG

Catches and triages ERP processing exceptions: missing fields, mismatched POs, blocked vendors.

Document Data Extractor
XV

Retrieves data elements from specified documents based on a query schema.

Spark Generation Job Builder
XV

Converts plain-English data requests into PySpark jobs. Executes on Databricks; returns the run output.

What do you want the agent to do?

Describe what you want the agent to do. Dataworkz will scaffold the persona, scenarios, and tools, using your connected enterprise data.

Build an agent to validate supplier invoices against vendor contracts.
Attach examples Choose a connector Uses your existing connectors + permissions
You can edit the result in the builder
Cancel Build agent
app.dataworkz.com/agents/invoice-validation/overview
InvoiceValidationAgent
e74976de…45a8
Live · last run 2m ago
Cancel Save Save & Publish
iAbout the agentA short description of the agent
Verifies invoices end-to-end: extracts header and line-item fields, matches against vendor contracts, validates hours and billing terms against ClarityServer, and routes ambiguous rows to a human approver.
pPersonaA short persona description
You are a helpful AI assistant specialized in invoice verification. Cite contract IDs and line numbers when explaining decisions. Defer to a human reviewer when budget tolerance is breached.
SScenarios
Add scenario
ExtractTimesheetDetails Reasoning
Invoice QNA and Summary Reasoning
Multi-Stage Invoice Verification Process Reasoning
Help / Greet / Exit Reasoning
ApprovalGate New · activates when invoice > $10K
app.dataworkz.com/agents/invoice-validation/roi
InvoiceValidationAgent
· Insights
Last Week
Analytics ROI Dashboard Conversation History
Measuring: Cost Saving · 24 months · Monthly AI cost: $5,900 · Monthly savings: $15,840 · Net: $9,940/mo Configure
Net Present Value
$203,608
Investment creates value
Payback Period
2 months
Quick payback
Total ROI
148%
Over 24 months
Net / month
$9,940
After agent costs
Cumulative Cash Flow Projection24 month horizon
Start from a template
Build from scratch
Type your request. Get a runnable agent.
Edit anytime with the AI builder
ROI per agent, measured per run

Trusted by

  • MongoDB
  • stagwell
  • within3
  • CockroachDB
  • Snowflake
  • Couchbase
  • Gowan
Every company has an AI mandate now. Not many start by asking what’s actually worth solving. Dataworkz did; then built the agents and showed us what each one returned.
Rahul Zutshi Program Manager

Every department has work waiting to be automated. Dataworkz gets it into production.

Most enterprise agents never make it past the demo. Dataworkz ships them: fast, governed, and measurable, across every workflow your business runs on.

01

Fragmented data

Enterprise data lives across thousands of systems. AI that can't reach it reliably can't do real work.

100+ pre-built connectors bring every system into reach. Your data stays where it lives.

02

No guardrails built in

LLMs are non-deterministic. Without structured reasoning and managed auth, you can't put them in front of enterprise workflows.

Deterministic or reasoned execution, managed auth, and a full audit trail, built into every agent.

03

Specialist dependency

Building a governed AI layer from scratch requires skills most teams don't have and timelines most budgets won't support.

Your existing developers build agents in plain English or code. No specialised AI hires required.

04

No visibility per workflow

Without agent-level observability and ROI tracking, you can't know what's working, what to fix, or what to fund next.

Every agent is measured: value, cost, and behavior tracked per workflow, not per program.

Fortune 200 AI story
Nine months of internal build work. Done in a day. Our engineers still own the workflow.
Engagement summary · Fortune 200 wealth management company

A Fortune 200 wealth management firm came to Dataworkz after a nine-month internal data lineage build stalled. The first agent went live in a day. They now run 20+ agents in production — across compliance, engineering, advisory, and operations — on the same platform.

Read the full story
Live · Regulatory data lineage agent
Lineage Documentation · Pipeline Lineage Graph
Analyzed · regulatory_reporting.py · 4,128 lines
DBpentaho.staging —▸ fxextract_lineage() —▸ ◎collibra.regulatory
2,847 columns traced Lineage complete
DBcobol.policy_master —▸ fxresolve_dependencies()
!tableau.q4_board_pack —▸ ◎regulator.export
⚠ 3 undocumented deps Audit-ready
Lineage Documentation found: 3 undocumented deps · 1 orphaned table · lineage report ready to export
In production
Time to first agent
9months 1dto ship

From a stalled nine-month internal build to a Dataworkz agent live in your environment in a day.

Production footprint
20+

Agents now running in production — across compliance, engineering, advisory, and operations.

Operational impact
$240K/day

Call-center cost baseline. 30% of high-volume calls deflected in the initial rollout.

One integrated platform. Your team configures instead of builds.

Watch one workflow run across your records: from your data, through a governed agent, to a full audit trail.

reconcile_batch_q4 · 1,200 records
Records · population of 1,200 Audit trail logged · every step
1,199 cleared · 1 exception · full audit trail. Run #4,712 · 1m 38s

Four layers. One platform.

The parts that made that workflow run.

01 · Connectors
100+ pre-built connectors

Cloud data, databases, SaaS, vector stores, and graph. Your data stays where it lives.

02 · Information Layer
Enterprise data, made AI-ready

RAG, knowledge graphs, chunk-level RBAC, and built-in evaluation.

03 · Agent Framework
Plain English in. Plans out.

Reasoned or deterministic execution. Your team owns the plan, A2A and MCP native.

04 · Operating Layer
Audit trail, auth, ROI tracking

Managed auth into your systems and per-agent ROI tracking. Observable from day one.

Explore the full platform

Start with the workflow that fits your team.

Every agent in the Dataworkz library is a live production workflow, not a template or a prototype. Pick one that maps to your department, configure it for your systems, and expand from there.

View full agent library
For engineering and data teams

Your developers own what gets built. That's not a feature. It's the architecture.

Dataworkz is not a black box. Your team defines the workflow, controls the execution plan, and inspects every tool call, decision, and outcome. Regular developers, on day one.

  • 01

    Plain English or code.

    Describe the workflow in natural language or write it directly. The same Agent Builder supports both. Your team picks the mode.

  • 02

    Deterministic or reasoning-based.

    Some workflows need a fixed plan. Others need to reason on the fly. Your team chooses, and both leave a full audit trail.

  • 03

    Full observability.

    Every source, tool call, decision, and exception is visible. Your security and compliance teams see exactly what each agent touches.

Explore the Agent Builder

Same need.
Two very different timelines.

Every enterprise we talk to has tried, or considered, building the platform themselves. This is what that path looks like, versus the one already running at a Fortune 200 firm.

The path most people take
to Build it yourself
Dataworkz
Months of integration work
  • 6–9 engineers, sequential dependencies
  • Prompts, evals, audit, built from scratch
Live on Day 1
  • Pre-built agent templates, ready to configure
  • 1 FTE to deploy and run
Another quarter per use case
  • Re-architect for every new workflow
  • Backlog grows faster than capacity
Same-day spinup
  • Add a workflow without touching the platform
  • Configure in natural language, not code
Custom-built audit, if at all
  • Hand-rolled logging and lineage
  • Brittle when models or sources change
Audited by default
  • Every decision sourced and replayable
  • Lineage built into the runtime
Enterprise controls bolted on
  • SSO, RBAC, VPC, figure it out
  • Compliance reviewed in arrears
SOC 2, VPC, RBAC. Day one.
  • Deploy in your VPC or ours
  • Compliance built in, not bolted on

Two paths. One is already in production at 20+ workflows across a Fortune 200 firm.

Built for your industry

Built for document-heavy, regulated, system-heavy work.

Three concrete workflows per industry. Start with what your teams know best.

01 · Financial Services

Financial Services

Advisors, assisted. Status calls, deflected. Regulatory lineage, documented.

Financial Advisor Assistant Call deflection Regulatory data lineage
Explore Financial Services
02 · Life Sciences

Life Sciences

Change requests, governed. Regulatory lineage, documented. Knowledge, retrieved.

Engineering Change Agent Regulatory submission lineage R&D knowledge retrieval
Explore Life Sciences
03 · Manufacturing & Supply Chain

Manufacturing & Supply Chain

Documents, extracted. Data pipelines, orchestrated. Exceptions, handled.

Customs classification Supplier invoice & PO recon ERP exception handling
Explore Manufacturing

Built for how enterprise AI has to ship.

The controls regulated buyers expect, in a single trust band.

01 · Deploy

3 deployment models

Managed SaaS, dedicated cloud, or in-customer VPC.

02 · Compliance

SOC 2 Type II + CASA Tier 2

Continuously compliant. Ready for regulated buyer review.

03 · Access

RBAC + managed auth

Role-based access. Governed auth for every system.

04 · Audit

Audit logging

Plans, tool calls, and outcomes captured per run.

05 · Trace

Traceability

Every source, decision, and exception inspectable end to end.

Get started

Bring one workflow. Leave with a path to production.

30 minutes. One workflow your team already owns. A demo grounded in your systems, your data, and your security model.

Talk to us
SOC 2 Type II, continuously compliant Deploy in your VPC Audit trail per agent