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AI Integration Services for Your Systems of Record

We connect AI to your ERP, CRM and legacy systems — with the write-back, permission and audit engineering that lets AI act, not just advise.

Credentials

ISO 27001 Certified

OraclePartner

SnowflakePartner

AWS Partner

Capability
16+
Years of Experience
250+
Projects Delivered
95%
Customer Retention
200+
AI-first Engineers
Who this is for

For CIOs and CTOs Whose AI Never Reaches the Workflow

Most AI stalls one step short of value — the model is right, but its output never reaches the system where work is recorded. So someone re-types it, and adoption dies by month two.

We close that gap. GrowExx engineers the write-back, permissions and audit trail that let AI act inside your ERP, CRM or core platform — not beside it.

How we put AI inside your systems

Three seams to close. Most projects close only the first.

01
Data & Identity

Trusted access to the right records, with entitlements that travel with every query.

02
Integration & Action

Write-back that respects transactions, approvals and reversal. Reading is easy. Acting is the job.

03
Evaluation & Operations

Shadow mode, drift and cost monitoring, so it stays correct after go-live.

What we deliver

Our AI Integration Services

AI Integration Assessment

We audit what your systems will actually permit — APIs, rate limits, transaction semantics — before anyone scopes a build.

ERP, CRM & Core System Integration

AI connected to Oracle, ERP, CRM and core banking, with write-back that respects transaction boundaries, approval chains and reversal paths.

LLM, RAG & Knowledge Integration

Retrieval over your documents and tickets, with permission-aware access so users only get answers from content they can already see.

Legacy & Non-API Integration

On-premise ERP, mainframe, file-based and acquired systems. Change-data-capture and façade patterns behind a stable contract.

Integration Security & Governance

Entitlement propagation, tamper-evident audit logs, prompt-injection testing and model version pinning — designed in, because retrofitting is impossible.

Integration Managed Services

Drift monitoring, model migration and contract tests, so an upstream upgrade fails in test rather than in production.

Industries we serve

AI integration, built for how your industry actually runs

Four focus industries. Each with a specific integration playbook.

The Challenge

The AI found the match, scored it and explained it. Now an analyst keys it into the ledger by hand — because nobody approves an AI posting without an approval chain and a reversal path.

How We Help

We build the write path. Matches post through existing maker-checker routing, every action carries its model version and approver, and systems run in shadow mode before they get authority.

Deliverables

Reconciliation posting with audit-grade evidence · Risk scoring inside existing credit workflows · Fraud detection wired into case management · Permission-aware adviser assistants

Quantified Result

30% fewer unexpected losses

Risk AI product for a US firm with $70B+ in assets.

The Challenge

The model says the bearing fails in eleven days. The prediction dies in a dashboard, because the maintenance system sits behind an OT boundary the integration was never designed to cross.

How We Help

We integrate with the OT/IT boundary rather than through it, running inference at the edge where line speed demands it. Predictions arrive as work orders inside the existing approval flow.

Deliverables

Forecasts written into planning and procurement · Predicted failures raised as work orders · Edge quality inspection posting to the quality record · Legacy MES integration without a modern API

Quantified Result

50% fewer forecasting errors.

AI forecasting and process optimization in specialty chemicals.

The Challenge

Summarization drafts the note in seconds and cannot write to the EHR. Write access is controlled by design, PHI cannot reach an outside model, and the interface is HL7 v2, not REST.

How We Help

We design so PHI never enters an uncontrolled AI workflow — de-identification and redaction at the context boundary, synthetic data in non-production, and private-tenancy or self-hosted models where policy requires. AI goes first at administrative and interoperability work, where return is fastest and clinical risk is lowest. Our ISO 13485 certification shapes how traceability evidence is produced.

Deliverables

Claims and prior-authorization processing · Documentation support written back under review · HL7 v2 to FHIR mapping and conformance testing · Revenue cycle exception detection

Quantified Result

90–100% extraction accuracy

OCR document capture for a US emergency medical service.

The Challenge

The allocation model is right and nobody runs it in December. Peak freeze locks every revenue path, and no one automates a margin decision that cannot be capped or reversed within the hour.

How We Help

We integrate into decisions that move margin, with guardrails that cap blast radius and incrementality measured before launch. Anything on a revenue path disables cleanly during freeze.

Deliverables

Forecasts and allocation written into OMS · Pricing integration under margin guardrails · Churn scores that trigger retention actions · Service automation that actions returns within policy.

Quantified Result

40% fewer stockouts

AI inventory forecasting, with 25% lower holding costs.

See how we cut a 15–20 day AP close to under 2 days.

Industry-specific reference architecture. 20 minutes. No slides.

How we deliver

Our AI Integration Methodology

Click any step to see what happens inside it and what you get out of it.

Step 01 · Discovery & Integration Audit

Find out what your systems will actually permit.

Workflow mapping plus a real integration surface audit per system — APIs, auth, rate limits, transaction semantics, freeze calendar.

Output: A feasibility assessment per system and a costed business case.

Step 02 · Architecture, Security & Eval

Design the write path and the guardrails before building.

Connectivity pattern per system, write-back and reversal design, entitlement propagation, and the golden datasets the integration is judged against.

Output: An architecture and control set your CTO and CISO can sign off.

Step 03 · Connectivity, Data & Identity Build

Build the pipes, resolve the entities, prove the permissions.

Connectors per system, entity resolution where systems disagree, retrieval indexing, and identity propagation verified by negative testing.

Output: Production connectivity with contract tests and a verified permission model.

Step 04 · Build, Action Engineering & Launch

Prove it holds — on accuracy, failure, cost and load.

The write path with idempotency and reversal. Then shadow mode against live traffic, compared to the existing process before authority is granted.

Output: A live integration with shadow-mode evidence and a reversible rollout.

Step 05 · Run & Compound

Keep it correct when models and source systems change.

Drift monitoring, cost per transaction, model migration as controlled change, and contract tests that catch upstream upgrades in test.

Output: A monitored estate on a quarterly outcome and cost review.

Why GrowExx

Why Choose GrowExx for AI Integration Services?

01

We Run the Systems Side, Not Just the AI Side

Oracle ERP and Financial Cloud consulting plus an automation practice, in-house. Most AI firms have never made a write succeed inside an ERP.

02

We Engineer the Write Path

Reading a system of record is a connector problem. Writing to one is a correctness problem — transactions, idempotency, a reversal path a controller accepts.

03

Permission-Aware by Design

We propagate the user's entitlements through retrieval rather than filtering output afterwards. It is the first question a CISO asks.

04

We Use the Middleware You Already Own

If you run Oracle Integration Cloud, MuleSoft or Boomi, we route through it — so you are not funding a second integration estate.

05

Patterns for Systems With No API

Mainframe, on-premise and acquired platforms. Change-data-capture and façade patterns — and we say plainly where a bridge is fragile.

06

95% client retention

95% of clients extend. Runbooks and dashboards ship as deliverables, so staying with GrowExx is a commercial decision, not a dependency.

Convinced? Schedule a 30-min AI integration roadmap call →

Selected work

Real-World AI Integration Case Studies & Success Stories 

Explore our latest case studies to see exactly how we deliver ROI for brands just like yours.

What our clients say

The CFOs and CIOs we've worked with.

"

GrowExx is the only partner who refused to start coding until we agreed on the ROI math. That discipline is exactly why our reconciliation agent shipped on time, on budget.

CFO
Global Finance Group
AI Agents
BFSI
"

We had three AI vendors. Two showed us demos. GrowExx showed us the eval harness, the cost-per-task dashboard, and the rollback plan. That's why they got the contract — and the renewal.

VP Operations
Apex Enterprise
Enterprise AI
Logistics
"

Their team treats every agent like a regulated piece of software, not a science project. That's the difference between a pilot you brag about and an agent your auditor signs off on.

Chief Risk Officer
Vanguard Manufacturing
AI Agents
Manufacturing

Talk to the team behind these outcomes.

From our insights

Where Enterprise AI actually pays

Featured Product

Recogent — AI integrated into an Oracle ERP, built by us.

Recogent reconciles GL, AP, AR and intercompany transactions — matching, routing exceptions and posting with audit-grade evidence. At a 100-store retail chain it runs against their Oracle ERP.

Recogent · Live metrics
99%
Invoice matching accuracy
<2 days
AP close, from 15–20
70%
Less manual work
Related services

Where to go next.

Enterprise AI Development

When the AI system itself has to be built, not just connected.

RPA, workflow platforms and system integration across the processes AI touches.

Embed AI inside the Oracle estate rather than integrating alongside it.

C-suite advisory: roadmap, ROI, governance, and where AI belongs first.

Frequently asked

FAQs about AI Integration Services

AI integration services connect AI models and agents to your existing systems, data and workflows — so AI can read the right information, act within the right permissions, write back into systems of record, and leave an auditable trail.

Development produces the model. Integration determines whether it can join a business process. If your problem is “we don’t have the AI,” that’s development. If it’s “the AI works but someone re-types its output,” that’s integration.

Through whatever the platform supports — REST, event streams, change-data-capture, or batch for older systems. What makes it safe is transaction and approval semantics, entitlement propagation, idempotency and reversal, and audit logging of every action.

Yes, and that is usually where the value is. We route AI actions through existing approval hierarchies, constrain them with deterministic rules, make them idempotent, and pair each with a defined reversal path. Many clients start read-only and widen authority in stages.

Yes — a large share of real integration work. We use change-data-capture, file pipelines with reconciliation, queue bridging, or an API façade so AI depends on a stable contract rather than legacy internals. Where a bridge is fragile, we say so and monitor it.

Usually yes. Routing through Oracle Integration Cloud, MuleSoft or Boomi keeps governance and support with the team that already owns integration. Exceptions exist — latency-sensitive inference, vector retrieval — and we make those explicit rather than leaving the cost to be discovered.

By propagating the user’s entitlements through retrieval and tool access, so answers can only be grounded in content that user can already see. Filtering generated text afterwards is not a reliable control. Verified by negative testing before go-live.

Two components, and only one is usually budgeted. Build cost tracks how many systems are involved and how mature their integration surfaces are. Run cost — inference per transaction, retrieval infrastructure, re-indexing, human review — is the line that surprises finance. We model it during architecture.

Discovery and integration audit typically runs two to four weeks, architecture a further two to four. A first workflow generally reaches shadow mode within a few months. The largest variable is the integration maturity of your systems.

Golden datasets covering real and edge cases, acceptance thresholds agreed with the process owner, and regression suites that assess outputs against criteria rather than exact strings. Integration paths — timeout, retry, partial failure, reversal — are tested deterministically.

Let's talk

Start Your AI Integration Project

If your AI works but nobody uses it, the problem is not the model. Bring the workflow and the systems behind it, and we will tell you what can be integrated and what it costs to run.

No sales pitch
NDA on request
senior AI architect on call

Fun & Lunch