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.
ISO 27001 Certified
OraclePartner
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AWS Partner
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.
Three seams to close. Most projects close only the first.
Trusted access to the right records, with entitlements that travel with every query.
Write-back that respects transactions, approvals and reversal. Reading is easy. Acting is the job.
Shadow mode, drift and cost monitoring, so it stays correct after go-live.
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.
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
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
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
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
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.
Our AI Integration Methodology
Click any step to see what happens inside it and what you get out of it.
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.
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.
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.
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.
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 Choose GrowExx for AI Integration Services?
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.
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.
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.
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.
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.
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 →
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.
Artificial Intelligence
Revolutionizing HR Policy Management: A Generative AI Solution for a Logistics Company
In the modern corporate setting, effective HR policy management is one of the key elements in ensuring organizational governance and contentment among employees while creating an environment that allows business to run smoothly. Client Overview…
Artificial Intelligence
Business Intelligence Solution built on Big Data for Internet Telephony Enterprise
Growexx provided a dedicated team that worked as an extended part for an MNC offering business intelligence solutions for big data analytics.
Artificial Intelligence
Funding Platform To Help Budding Musicians
GrowExx helped in launching a funding platform to help budding musicians with no strings attached.
Artificial Intelligence
Creating a Product Roadmap for AI-powered Career Counselling System
GrowExx team held a product discovery session to chalk out a product roadmap to create an AI-powered career counselling system.
Artificial Intelligence
Digitizing Culinary Heritage: Transforming Handwritten Reviews with NLP
In the heart of Paris, a leading restaurant that has been operating for decades faced a challenge. The reviews by customers were hand-written about their experience at the eatery. Thus, there was a need…
Artificial Intelligence
From Bidding to Winning: The Tender Automation Success Story
In this fast-paced environment of tender acquisition, precision is the keynote to success. This study highlights the transformative partnership between a leading IT Hardware & Networking company and GrowExx, and how innovative solutions completely transformed…
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.
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.
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.
Talk to the team behind these outcomes.
Where Enterprise AI actually pays
Agentic AI
AI Agent Identity and Permissions: The Overlooked Design Problem
An AI agent’s identity is the credential it authenticates with when it calls a system, and its permissions are the entitlements attached to that credential. In most pilots neither is designed: the agent runs with a developer’s token, a shared…
Agentic AI
AI Agent Memory: Short-Term, Long-Term and When You Need It
AI agent memory is the set of mechanisms that carry information between a model’s calls: the transcript re-sent on each turn, the state an orchestrator holds for a session, the facts written to durable storage, and the index that retrieves…
Agentic AI
Single-Agent vs Multi-Agent Systems: How to Choose
A single-agent system is one model loop that plans, calls tools and observes results until a task is finished. A multi-agent system splits that work across several loops that pass control, context and results between them. The difference is not…
AI
Securing Enterprise GenAI: The Controls That Hold at Production Scale
Securing an enterprise GenAI system means controlling what it can reach, what it can do, and what it can reveal — because the model itself cannot be relied on to enforce any of those. The controls sit around the model,…
AI
Building GenAI Products at Scale: What Breaks Between Ten Users and Ten Thousand
Generative AI product development at scale means solving five problems that do not exist at pilot size: unit cost that grows with usage, evaluation that has to run as release infrastructure, model versions that get deprecated underneath you, tenant data…
AI
Can an LLM Read Your Financial Statements?
A large language model can read a financial statement and produce a plausible analysis of it. Whether that analysis is usable in a controlled finance function depends on something the model does not provide: a way to prove each figure…
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.
Where to go next.
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.
