Paid activation
AIGIS-hosted
Request an AIGIS-hosted activation; runtime work starts only after paid approval.
- Paid commercial activation first
- BYO production model keys
- Runtime provisioned after approval
Salesforce live today. ServiceNow and SAP in design-partner co-dev.
One AI across the entire enterprise.
Governed by permissions, backed by an audit log. AIGIS enforces live permissions before AI reads or writes, then records every action taken by a user or by AI acting as that user.
SOC 2 Type I in audit·Salesforce in production·ServiceNow + SAP design-partner co-dev·4 provisional patents·Open governance schema (publishing 2026 Q3)
What happens in the first use case
1
A sales VP asks which renewal accounts are at risk this quarter.
2
AIGIS maps identity into each approved system and checks native permissions.
3
Unauthorized fields are removed before model context exists.
4
APIs, cache, SQL, or OData answer what they can; an LLM synthesizes only when needed.
5
The answer returns with provenance showing what was included and why.
Live today
Salesforce in production
Field-level permission enforcement, governed read/write, write-time recheck, and an auditor-grade provenance row for every request
Design-partner co-dev
ServiceNow + SAP
Connectors under active co-development with design partners; not GA, not pretending to be
The moat
Provenance ledger
Open governance schema (publishing 2026 Q3): every model call has a defensible audit row your CISO can show the board
The problem
Enterprise AI is being trapped inside vendor silos.
The next enterprise interface should not belong to Salesforce, SAP, ServiceNow, Microsoft, or any one LLM provider. It should belong to the enterprise.
Intelligence is trapped in vendor front ends
Agentforce centers Salesforce. Joule centers SAP. Now Assist centers ServiceNow. The enterprise ends up with separate AI layers for work that crosses the same business process.
Every request becomes a premium AI event
Vendor assistants price and meter as if every user request needs their AI. In reality, many requests should hit cache, a live API, SQL, OData, or a workflow before a model is involved.
Governance happens too late
Masking sensitive fields after retrieval still tells the model the field exists. A headless enterprise needs permission-aware context construction before any prompt is built.
The solution
Separate enterprise intelligence from enterprise vendors.
AIGIS sits above your systems of record and below your AI of choice. It is the neutral control plane that decides what can be accessed, which path should execute, and what evidence gets logged.
Vendor-selectable systems.
Connect systems with APIs and permission surfaces: Salesforce, SAP, ServiceNow, databases, data warehouses, and custom internal apps.
Governance before intelligence.
AIGIS resolves identity, rebuilds permissions, strips inaccessible fields, and logs provenance before context reaches a model.
LLMs as swappable compute.
Claude, GPT, Gemini, open-source, self-hosted, or none at all. AIGIS routes each request to the cheapest safe execution path.
Control plane
Not every request needs a model.
AIGIS sits between chat surfaces and systems of record. Requests can start in Slack, Claude, Teams, OpenAI, or Salesforce LWC, then pass through the same governed routing layer before anything is read, written, or sent to a model.
Systems of record
SalesforceAPI
SAPAPI
ServiceNowAPI
DatabasesAPI
Custom APIsAPI
AIGIS MCP
Governed router
One governed interface for every request surface
Identity map
Permission model
Field stripping
Live record checks
Provenance ledger
Chat surfaces
Slack
Claude
Teams
OpenAI
SF LWC
Execution routing after governance
Cache
Known answer, governed data hash matches
Workflow
Registered action or human-approved write
Live query
SOQL, OData, SQL, or system API
Model
Small, frontier, fallback, or customer-hosted LLM
Setup paths
Hosted, hybrid, or customer-hosted from the same governed product.
AIGIS is isolated by design. Teams can choose a paid managed launch, move the data-touching runtime into their own cloud, or run the governed data plane fully inside their boundary.
Paid hybrid activation
Hybrid
Request a hybrid activation; Governed operates the experience while your sensitive data plane runs in your cloud.
- Customer-hosted MCP runtime
- BYO production model keys
- Optional audit replication
Paid private activation
Customer-hosted
Request a customer-hosted activation; your team runs the full governed data plane in your environment.
- Dedicated runtime boundary
- Customer-managed database and audit store
- Deployment kit and upgrade path
The economics
AI cost falls when AI stops being the default.
Vendor assistants monetize every interaction as an AI event. AIGIS treats LLMs as one execution option behind a governed router, so more enterprise knowledge means fewer expensive model calls.
Request type
Vendor AI path
AIGIS path
Model cost
Repeat answer
New AI conversation
Governed cache
No model call
Structured lookup
Premium assistant
Live API / SQL / OData
No synthesis unless needed
Routine reasoning
Vendor default model
Small or cheap model
Task-fit routing
Ambiguous executive brief
Same vendor AI
Frontier model
Reserved for hard work
Vendor stack / 50K users
Pricing model
Annual cost
Agentforce Employee Add-On
$125 to $150 / user / mo
$75M to $90M
ServiceNow Now Assist
$50 to $100 / fulfiller / mo
$30M to $60M
SAP Joule Premium
Per AI Unit, consumption
$2M to $20M
Microsoft 365 Copilot Enterprise
$30 / user / mo
$18M
MuleSoft (cross-system enabler)
Annual platform license
$2M to $5M
Stack total
Multi-vendor reality
$127M to $193M
AIGIS Scale
Platform plus governed routing
$600K to $1.5M
Sources: Salesforce and Microsoft publish list pricing. ServiceNow, SAP Joule, and MuleSoft are quote-based or consumption-based, so those ranges are industry estimates. Production model spend is paid through the customer's provider account; AIGIS reduces that spend with cache, direct API/database paths, and model routing policy.
The defensible moat
Most AI masks. AIGIS strips.
Masking protects the value of a field. Stripping protects the existence of the field. The difference is architectural, and it shows up the moment a model is asked to reason about your data.
Industry default
Masking
What the LLM sees:
- Account.NameAcme Corp
- Account.AnnualRevenue[MASKED]
- Contact.SSN__c[MASKED]
- Account.OwnerJ. Smith
The model still knows SSN__c and AnnualRevenue exist. It can reason about their position, infer relationships, and leak structural metadata in its response.
AIGIS approach
Stripping
What the LLM sees:
- Account.NameAcme Corp
- Account.OwnerJ. Smith
- fields the user cannot access are architecturally absent
The model has no way to know SSN__c exists. No metadata leakage. No structural inference. The data is architecturally absent before the prompt is constructed.
For the technically curious: Salesforce Agentforce honors object and field-level security at query time. AIGIS adds a second layer that removes fields from the prompt context before the LLM ever sees them, so model output cannot leak structural metadata. Both approaches enforce permissions. Only one enforces them on the model itself.
Why AIGIS wins
The headless enterprise needs a control plane.
Connectors alone create sprawl. Chatbots alone create risk. AIGIS combines routing, permission reconstruction, model choice, and audit evidence in one governed layer.
Vendor freedom
Choose the enterprise systems that fit the business. AIGIS sits above them as the governed interface instead of forcing intelligence into one vendor's application layer.
Permission emulation
When native security is available, AIGIS enforces it. When data moves to a warehouse or replica, AIGIS recreates table, column, and row permissions before AI access.
Multi-request orchestration
One prompt can become several governed operations across Salesforce, SAP, ServiceNow, databases, and workflows, each with per-system provenance.
Confirmation-time writes
AIGIS never treats approval as stale. It rechecks object, field, and record permissions at the moment the user confirms the mutation.
Defensibility
The control plane is defensible.
Three provisional patents pending across eight inventions, plus five trade secrets protecting the internal architecture behind permission-aware prompt construction, write safety, and multi-system orchestration.
8
Inventions filed
3
Provisional patents pending
5
Trade secrets
Filing 01
Governance before prompts
Permission-aware context construction across heterogeneous enterprise systems before data reaches a model.
Filing 02
Confirmation-time writes
Mechanisms that keep AI-generated mutations governed when the user confirms the action.
Filing 03
Headless orchestration
Routing intent across multiple systems while preserving each system's security posture and audit trail.
Demo scope
Bring one governed workflow into sharp focus.
Use the first conversation to test whether one governed workflow deserves a paid proof sprint.
Setup path
Review
Cohort
2026 Q2 to Q4
- Inspect the live Salesforce permission path and provenance row
- Identify whether ServiceNow or SAP belongs in design-partner scope
- Map the runtime, secrets, audit, and model-key boundary
- Leave with a proof sprint plan only if the workflow is a real fit
Paid proof sprint scope is set only after the workspace and buyer boundary are clear.
Next step
Put one governed workflow under review.
Use the demo to inspect the Salesforce permission path, the provenance ledger, and the current ServiceNow and SAP design-partner boundaries.