Enterprise AI agents

Production-ready AI agents on data you can actually trust.

DataGOL connects, governs, and orchestrates your enterprise data so agents ship reliably, follow your policies, and leave a full audit trail. Pre-built agents run on day one, and governed custom agents reach production in 8 to 10 weeks.

Governed data · Reliable agents
ConnectGovernOrchestrateAudit
78%

of enterprise AI initiatives never reach production at scale

14%

of enterprises have scaled an agent into production

$340K

average direct cost of a failed AI agent initiative

61%

of failures trace back to the data layer

Why pilots stall

Agents succeed or fail at the data layer.

Demos run on clean, curated data. Production data has missing fields, conflicting definitions, and no permissions model. Check your environment against these seven requirements before scoping an agent. If more than two show a gap, fix the data layer first.

RequirementWhat it meansCommon gap
Connected sourcesEvery system the agent needs is integrated and accessible.CRM, finance, and ops sit in silos with no shared identifier.
CompletenessOver 95% of required fields are populated across source records.15 to 40% of production records miss critical fields.
FreshnessData age stays within the agent's decision window.Pipelines refresh nightly while agents need real time.
Consistent definitions"Revenue," "active customer," and "churn" each mean one thing.Finance and Sales define terms differently, so the agent guesses.
Permissioned accessThe agent reads only what it is authorized to access.No row-level security, so users see data they shouldn't.
Cross-system joinsA common identifier resolves entities across sources.No shared customer ID across CRM, billing, and support.
Audit trailEvery action is logged with the data state that triggered it.No traceability, so agent behavior can't be explained or fixed.
Architecture

Two tiers that make agents reliable.

ContextOS makes enterprise data AI-ready. AgentX runs governed agents on top of it. Every DataGOL agent, pre-built or custom, runs on both.

ContextOS

The shared context layer

Combines structured data, documents, and operational history so every answer and action reflects how your business actually works.

  • 100+ pre-built connectors to Snowflake, Salesforce, Stripe, NetSuite, BigQuery, HubSpot, and custom APIs, live within minutes.
  • Shared definitions so "revenue" means one thing across every system.
  • Knowledge graph and shared memory so agents reason about relationships and business rules.
  • Row-level permissions defined once and honored by every agent.

AgentX

The governed execution layer

Runs specialized agents with defined roles, authority levels, and goals, verified at every step.

  • Multi-agent orchestration that coordinates specialized agents with no custom harness.
  • Approval routing that sends high-stakes actions to human review.
  • AI Firewall guardrails that enforce policy before and after every action, with real-time monitoring.
  • Immutable audit logs that record each action with the data and decision path behind it.
Deploy anywhere:Cloud VPCOn-premBare metalGovCloudAir-gapped
Pre-built agents

Agents you can deploy on day one.

Use them out of the box, customize them, or compose them into workflows for your vertical.

Advanced reasoning

  • Data Science
  • SQL
  • Knowledge Graph
  • GRC

Intelligence and search

  • RAG
  • AI Search
  • Data Conversation

Analytics and insight

  • Deep Research
  • BI / Chart

Data quality

  • Data Cleaning

Integration and action

  • HubSpot
  • Jira
  • Salesforce

Every result shows its work. The Data Science Agent, for example, confirms your intent, shows the plan, data, and Python code it will run, then returns charts and conclusions with reproducible logic.

How it works

From connected data to automated action.

Connector library including Snowflake, PostgreSQL, NetSuite, HubSpot, Shopify and Stripe
In production

Real customers. Measured outcomes.

Each of these started by solving the data layer. The agent worked from there.

Customer experience

AI-powered QA scoring

Every call and email scored automatically, replacing a 30-analyst QA team.

100%Conversations scored
8×QA coverage
<24hCoaching delivery
Compliance

Automated security questionnaires

200-question healthcare security questionnaires auto-filled with cited, audit-trailed answers.

90%Fields auto-filled
2d → 2hPer questionnaire
100%Responses cited
Banking GRC

Agentic governance, risk and compliance

Bank-grade controls mapped continuously and fully auditable.

340+Controls mapped
7×Faster audit prep
100%VPC-deployable
Healthcare

Enterprise search for LHC

Executives ask a 412-page directory anything and get cited answers in seconds.

~2sTime to answer
3,400Queries answered
412pgDirectory indexed
Data engineering

Data agents for events and ticketing

Triple the workbook throughput with zero hand-written SQL.

3×Workbook throughput
65%Fewer data tickets
0Hand-rolled SQL
Growth

Unified growth analytics for gaming

Growth questions in plain English with auditable, lineage-traced answers.

$180kBudget reallocated
3×Faster decisions
1Source of truth

Hear directly from our clients.

"It's allowed us to put in front of our attendees a more modern experience. It's a good reflection of our company. It's just a more sophisticated approach all around."
Amy RafaelMarketing, Lincoln Healthcare Leadership
"They've been phenomenal, extremely helpful, extremely enthusiastic, very professional, very pragmatic and visionary."
David JacobsCo-Founder and CEO, Change Partners
"They cut our implementation time in half, and the total cost was one-tenth of what everyone else was quoting."
Hoyin CheungFounder of Remo and Former CEO
"DataGOL helped lift a major operational and technical burden off our team."
Raymond DongAvantis
"Delighted with the team, delighted with product. It seems to improve on an almost daily basis."
Alex SaintyAngel Investor
Compare

Built for the data layer first.

Orchestration-first platforms demo quickly on clean data. DataGOL solves the data and agent layers together.

DataGOLStackAILyzrDataiku
Data layerBuilt in. ContextOS connects, models, and governs enterprise data.Assumes data is already clean.Assumes data is prepared.Strong prep, separate from orchestration.
OrchestrationAgentX with roles, approval routing, and audit logs.Workflow builder with API integrations.Framework with pre-built templates.Agent recipes for data science workflows.
GovernanceImmutable logs, AI Firewall, row-level permissions in both layers.Workflow-level logging.Output-quality focused.Strong for ML, lighter on agents.
DeploymentCloud VPC, on-prem, bare metal, GovCloud, air-gapped.Primarily cloud-hosted.Cloud-hosted.Cloud and on-prem.
Time to production8 to 10 weeks for governed production.Fast for simple workflows; data prep is external.Fast for templates; data prep is external.Longer enterprise cycles.
Best fitEnterprises that need data and agents solved together.Teams with clean data needing automation.Teams wanting templates on prepared data.Data science teams extending ML.
Deployment

A governed agent in production in 8 to 10 weeks.

Deployments that reach production follow one order: data first, agent second.

  1. Weeks 1 to 2

    Data audit

    Map every system the agent needs and run the readiness check. You get a clear list of gaps to close.

  2. Weeks 3 to 4

    ContextOS setup

    Connect sources, build shared definitions, set permissions, and start the audit trail.

  3. Weeks 5 to 6

    AgentX setup

    Define role, scope, and tools. Configure approvals and guardrails, then test on production data.

  4. Weeks 7 to 8

    Review and pilot

    Security review, audit log validation, and access checks. The agent goes live on real traffic.

  5. Weeks 9 to 10

    Measure and expand

    Measure against baseline and get an evidence-based plan for the next agents.

FAQ

Frequently asked questions.

What is an enterprise AI agent?

Software that reasons over business data, uses tools, and completes multi-step work inside defined guardrails. "Enterprise" means it runs with governed access, audit logs, role-based behavior, and production reliability at volume.

What AI agents does DataGOL offer?

Pre-built agents for data science, SQL, knowledge graphs, GRC, RAG, AI search, deep research, BI and charts, data cleaning, and HubSpot, Jira, and Salesforce actions. You can also build custom agents or compose them into vertical workflows.

What is the difference between ContextOS and AgentX?

ContextOS is the data layer. It connects sources, sets shared definitions, and enforces permissions. AgentX is the execution layer. It runs agents with roles, approval routing, guardrails, and immutable audit logs using the trusted context from ContextOS.

Why do most enterprise AI agent pilots fail?

61% of failures trace back to data: fragmented sources, conflicting definitions, missing permissions, and no audit trail. DataGOL solves these first so the agent works reliably on real production data.

Will it work with our existing systems?

Yes. 100+ pre-built connectors cover warehouses, CRMs, ERPs, finance, and marketing tools, plus custom APIs. Most connectors go live within minutes of signup.

Do we need an AI or engineering team to start?

Pre-built agents and plain-English questions work from day one. For governed custom agents, DataGOL runs the 8 to 10 week deployment with your team, starting from a data audit.

How do you handle security and compliance?

Row-level permissions, AI Firewall guardrails, real-time monitoring, and immutable audit logs come standard. DataGOL deploys in cloud VPC, on-prem, bare metal, GovCloud, or air-gapped environments, and is SOC 2 compliant.

Get started

Your agents are ready. Are you?

Connect your data, deploy your first agent, and get your first insight in under four minutes.

Start shipping your AI features in days, NOT months.