Enterprise Connectivity

AI Integration — woven into the systems you already run.

We connect large language models into your existing ERP, CRM, and internal tools through a zero-trust middleware layer — schema-enforced, fully traced, and built to pass a security review the first time.

See the case study ↓
99.9%Uptime SLA
<200msProxy overhead
72%Faster lookups*
All evals passing
elhaa · integration layer
1Client App / Legacy ERP
2Zero-Trust Middleware
3Model Provider
4Enterprise Database
<200ms overhead
What's included

A complete integration layer, not just an API call.

Not just a connector — the full layer of security, monitoring, and handover that makes it production-grade.

Zero-trust API proxy with rate limiting & fallback circuit breakers

Every call authenticated, throttled, and automatically rerouted to a fallback model if the primary times out.

02

Schema enforcement & function calling

Custom JSON schema validation on every response, with dynamic prompt function calling.

03

Async job queuing

High-throughput batch workloads processed off the request path, with retries built in.

04

Full OpenTelemetry tracing

Every request traced end-to-end across every API boundary — visible in your existing observability stack.

05

Admin & access console

See what's calling what, revoke access instantly, and audit every request without touching code.

Engineering deep dive

How it's actually built.

The topology and the production code pattern — not a simplified marketing diagram.

1Client Application / Legacy ERP
2elhaa Zero-Trust Middleware & Schema Enforcer
3Model Provider (Gemini / OpenAI / Anthropic)
4Internal Enterprise Database / Storage
resilient-proxy.ts
// elhaa Resilient Proxy & Schema Enforcer
const response = await elhaaProxy.execute({
  model: 'gemini-2.5-flash',
  messages: [{ role: 'user', content: prompt }],
  responseSchema: OutputSchema,
  fallbackModel: 'claude-3-5-sonnet',
  timeoutMs: 1200
});
Case study

Order-Status Copilot on a Legacy ERP

Manufacturing · Industrial equipment manufacturer

The challenge

Support reps spent 8–12 minutes per call navigating legacy SAP screens and running manual database queries just to answer “where's my order.”

The approach

A RAG plus text-to-SQL hybrid assistant with strict schema guardrails, a read-only replica connection, and automated caching — reading from SAP, never writing without a human.

Natural language → intent classifier → read-only schema-constrained SQL generator → SAP DB replica → answer synthesis
72%Reduction in call handle time
99.1%First-pass query accuracy
420Support reps onboarded

*Illustrative example based on a representative engagement.

The difference

The typical approach vs the elhaa approach.

Typical approach
With elhaa
Starting point
Buy a new AI platform and migrate to it
AI added to the systems you already run
Security
Reviewed after build — causing rework
Access rules designed in before any code
Vendor choice
Locked to one model provider
Routing layer — switch models anytime
Success measure
“The feature launched”
Adoption and time saved, measured weekly
How the engagement runs

Four steps, six to twelve weeks.

1

Map

Inventory your systems, data, and the workflows where AI adds measurable value.

2

Design

Choose models, define interfaces, and agree security and access boundaries.

3

Integrate

Build and test connectors against real data in a staged environment.

4

Adopt

Roll out to teams with training, feedback loops, and usage measurement.

How success is measured

Agreed in week one, on a dashboard by go-live.

Adoption

Weekly active users

How many people actually use the integration — the truest measure of fit.

Speed

Task time saved

Minutes per task before vs after, sampled from real workflows.

Quality

Eval pass rate

Accuracy of AI outputs against the agreed evaluation suite.

Economics

Cost per request

Fully loaded inference cost, tracked against the budget model.

Works with your tools

Typical systems & standards.

SalesforceSAPZohoHubSpotServiceNowJiraMicrosoft 365Google WorkspaceSlack & TeamsREST & GraphQL APIsLegacy databases
Who's involved

Small teams on both sides.

From elhaa
  • Solution architectOwns the integration design and security model.
  • Integration engineersBuild and test the connectors and copilots.
  • Evaluation engineerBuilds the test suite that gates every release.
From your side
  • System owner2–4 hours a week for access, context, and decisions.
  • Security reviewerApproves the access model early, not after build.
  • Pilot users2–3 people who use it daily and give blunt feedback.
FAQ

Questions about AI Integration.

Anything with an API or database access — common ones include Salesforce, SAP, Zoho, HubSpot, ServiceNow, Jira, Google Workspace, Microsoft 365, and custom internal tools. Where no API exists, we design around exports or middleware.

No. We design vendor-neutral architectures with a routing layer, so you can use different models for different tasks and switch providers without rebuilding.

A focused first integration typically ships in 4–8 weeks, depending on system access and security review timelines on your side.

Usually, yes. Where there's no modern API we work through database views, file exports, message queues, or lightweight middleware — and we're upfront in the Design phase if a system genuinely can't be reached safely.

By meeting people where they work: the AI appears inside existing tools rather than as a new destination. We pair that with champion users, short training sessions, and usage dashboards so you can see adoption rather than assume it.

Sounds like your situation?

A 30-minute call. We'll tell you honestly whether this is the right solution — and what it would take.

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