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AI strategy and decision mapping
Find the decisions worth automating first, rank them by value and risk, and set the boundaries for each.
Enterprise AI services
Enterprise AI that makes it to production, and stays there.
We design, build and run AI agents on your data and systems, with the controls your risk, audit and security teams need. Every agent runs on Context OS: business context in, policy checked, every decision traced.
- Managed SaaS
- Your VPC
- On-prem and hybrid
Trust graduation
Autonomy is earned
ShadowAgent recommends; people decide
Today
Context OS · decision layer
- Supervised
- Bounded
- Decision traces
Autonomy rises only where traced accuracy earns it.
Full autonomyEarned through traced accuracy
Earned
Works with
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What is enterprise AI?
AI that runs inside the business, not beside it
Enterprise AI is the use of machine learning, language models and AI agents inside an organization's core systems, such as ERP, CRM and data platforms, to automate work and improve decisions at scale.
Unlike consumer AI tools, it has to work on proprietary data, follow access rules and regulations, and explain what it did. Most pilots stall between demo and production because no one can show what an agent knew, what it was allowed to do, and why it acted.
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01
Scattered context
Data, documents and rules sit in different systems, so agents decide on incomplete information.
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02
No authority check
Nothing confirms an agent is allowed to take an action before it runs.
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03
No decision record
Auditors and regulators get logs, not reasons.
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04
Rising cost, unclear ROI
Token spend grows while the business case stays a slide.
Our services
From strategy to agents in production
One team, one platform, accountable for outcomes, not hours.
Data and business context
Connect your systems seamlessly with Unify Data and model how your business works with Business Context for better decision-making.
Agent design and build
Design and build intelligent agents for operations, finance, risk and service, powered by the models you choose.
Governance and controls
Policy and authority checks before every action, through governed agentic actions and decision traces.
Private and sovereign AI
Run assistants and agents in your VPC or on-prem, with data kept in-region and a council of models checking each other.
AgentOps and managed service
Registry, identity, evaluation and monitoring for every agent, with SLAs and support after go-live.
How we deliver
First agent in production in weeks, not quarters
Pick three decisions, deploy Context OS, run agents in shadow, then raise autonomy where decision traces prove accuracy.
Map
Pick three decisions, their data, their owners and their limits.
Deploy
Context OS goes live in your environment, connected to your systems.
Shadow
Agents run alongside your teams while decision traces prove accuracy.
Scale
Raise autonomy where it is earned, and add the next decisions.
What's different
The model works. The enterprise around it isn't ready.
A pilot stops at the demo. Production needs context, a check before every action, and a record of why the agent acted.
| Typical AI pilot | ElixirData approach | |
|---|---|---|
| Context | Data, documents and rules sit in different systems | Connected to your systems of record and documents |
| Authority | Nothing confirms an agent may act | Policy checked before every action |
| Record | Auditors get logs, not reasons | A decision trace for every action |
| Cost | Token spend grows while ROI stays a slide | Lower token cost, with the outcome accounted for |
| Autonomy | Stuck after the pilot | Earned from shadow mode to full autonomy |
| Time to value | A later project, after the demo | First agent in production in weeks |
Where enterprise AI pays off
Agents for the work that runs your business
Operations, security, risk and finance are where a governed agent can take work on first.
01
Operations and SRE
Incident triage, root cause and remediation.
Explore solution 02Security and SOC
Alert triage and response within approved playbooks.
Explore solution 03Risk and compliance
Continuous control checks and audit-ready evidence.
Explore solution 04Finance and procurement
Approvals, exceptions and spend control in your ERP.
Explore solutionResults
Results our platform delivers
FAQ
Enterprise AI, answered
Straight answers on why pilots stall, how fast you reach production and how autonomy is earned.
No one can show what the agent knew, what it was allowed to do, and why it acted.
The first agent typically runs in production within weeks: map, deploy, shadow, scale.
Agents start in shadow mode. Autonomy rises only where decision traces prove accuracy.
The models you choose, hosted as managed SaaS, in your VPC, or on-prem.
Context OS sends agents only the context each decision needs, which lowers token spend.
Start with an AI decision assessment
We map the decisions worth automating first, their data and limits, then design the path to your first agent in production.