Enterprise AI Development That Reaches Production
We build enterprise AI solutions that run inside your ERP, data platforms, and core workflows — with the integration, evaluation, and cost controls that keep them live after the pilot.
ISO 27001 Certified
OraclePartner
SnowflakePartner
AWS Partner
For Leaders Whose AI Pilots Never Reached Production
Enterprise AI rarely fails at the model. It fails at the seam between the model and the system of record — the master data that disagrees across three systems, the write-back nobody scoped, the evaluation baseline nobody set, and the process owner nobody named. The result is a portfolio of pilots that impressed a steering committee and reached no one.
GrowExx works the other way around: we start from the workflow and the data, treat integration into your ERP or core platform as the center of the engagement rather than the last phase, and design evaluation and governance before we write application code. What you get is enterprise AI that clears your own architecture review, survives audit, and reports its cost per transaction.
Three layers. Skip one and it stays a pilot.
Decision mapping, master data, pipelines, retrieval — the ground truth AI runs on.
Models, orchestration, and write-back into ERP, ledger, and core systems of record.
Golden datasets, audit trails, drift and cost monitoring — proof it works, and stays working.
Our Enterprise AI Development Services
AI Strategy & Data Readiness Assessment
We rank candidate use cases by value and feasibility, profile whether your data can actually support them, and check what your existing platform licences already provide. The output is a costed, sequenced roadmap — and a defensible reason for what you are not building.
Custom Enterprise AI Development
Forecasting, scoring, anomaly detection, document intelligence, optimization, and generative AI — built for one named business process, integrated into the systems that run it, and measured against a baseline captured before the build started.
Enterprise AI Integration
The engineering that decides whether AI can act or only advise: write-back that respects transaction boundaries and approval chains, entitlement propagation so AI operates within the user's permissions, and audit logging on every decision. Oracle, SAP, Salesforce, ServiceNow.
Generative AI & Knowledge Systems
Retrieval pipelines over your documents, tickets, contracts, and policy corpora — with permission-aware retrieval, source citation, abstention behavior, and a golden-question evaluation set. Users get sourced answers from content they are entitled to see.
AI Agents & Workflow Automation
Agents that execute multi-step processes across systems with defined authority, scoped tool permissions, human approval gates by value threshold, and idempotent retry and rollback. Order-to-cash exceptions, invoice matching, close support, service triage.
AI Platform & Production Operations
The shared layer that makes use case three cheaper than use case one: model gateway with provider abstraction, reusable evaluation tooling, cost-per-transaction observability, drift monitoring, and controlled model-version migration. Plus the managed service to run it.
Enterprise AI, built for how your industry actually runs
Four focus industries. Each with its own constraint to design around.
The Challenge
A risk analyst has a model that improves the portfolio outcome and cannot explain it to validation. So it sits. Meanwhile reconciliation runs twenty days on manual matching, KYC refresh is a queue of documents, and the fraud alerts that do fire arrive faster than investigators can clear them. Every automated decision that touches a customer is one a regulator may later ask you to reproduce.
How Our Enterprise AI Helps
We build for defensibility as well as accuracy. Deterministic rules stay wrapped around probabilistic components so policy limits cannot be breached by a model. Acceptance thresholds are agreed with risk and validation before deployment, not after. Every decision logs its inputs, sources, model version, and any human override — and data stays inside approved boundaries, including private-tenancy models.
Deliverables
Risk and early-warning models with explainability artifacts. Reconciliation and exception automation with audit-grade evidence. KYC and claims document intelligence. Permission-aware advisor assistants.
Quantified Result
Risk AI product for a US firm with $70B+ in assets.
The Challenge
A planner rebuilds the same demand scenario in a spreadsheet because the planning system cannot answer “what if the supplier slips a week.” Two buildings over, quality data sits in a historian the corporate network is deliberately segmented from. The data exists in volume; almost none of it is joined, labeled, or trusted across sites — and nothing that touches the line can be trialed in production.
How Our Enterprise AI Helps
We design around the OT/IT boundary rather than against it — training on historian and ERP data without breaching segmentation, and running inference at the edge where latency demands it. Forecasting is backtested against your own history so the business case is proven before deployment, and anything touching the line runs in shadow mode until it clears an agreed accuracy threshold.
Deliverables
Demand forecasting and inventory optimization wired into planning. Process parameter and yield optimization. Predictive maintenance with work-order integration. Quality inspection at the edge.
Quantified Result
With 65% productivity gain, in specialty chemicals.
The Challenge
The administrative work is drowning the clinical work — prior authorization packets, denial rework, coding queues, and an interoperability backlog of HL7 and FHIR mappings where a single wrong field silently corrupts a feed. The obvious AI answer is blocked by the obvious constraint: IT will not approve anything that lets protected health information touch an outside model.
How Our Enterprise AI Helps
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 document processing with denial analysis. Coding and documentation support under human review. HL7-to-FHIR mapping with conformance tests. Revenue cycle exception detection.
Quantified Result
at a specialty-care network.
The Challenge
Pricing, allocation, and promotion are the decisions that actually move margin, and they are the ones nobody wants to automate — because a wrong call is visible in the P&L that week. Customer data is split across channels, so personalization runs on a partial view of the same person. And peak trading imposes a freeze that compresses the delivery year into the months when everything is riskiest.
How Our Enterprise AI Helps
We target the margin and working-capital decisions rather than front-end novelty, and we insist on measurement design before launch — holdout groups, incrementality testing, and guardrails that cap the blast radius of an automated pricing or allocation call. Identity resolution across channels is scoped as real work, not assumed. Systems on revenue paths degrade gracefully and switch off cleanly.
Deliverables
Demand forecasting and cross-channel inventory allocation. Pricing and promotion optimization with margin guardrails. Churn prediction with retention triggers. Catalog enrichment.
Quantified Result
With 25% lower holding costs, in AI Inventory Forecasting.
See how we cut a 20-day reconciliation cycle to 2 days for a finance team.
Industry-specific reference architecture. 20 minutes. No slides.
Our Enterprise AI Development Methodology
Click any step to see what happens inside it and what you get out of it.
Find out if your data can support the use case — before the build.
Workflow and decision mapping, baseline cost capture, data quality and master data profiling, integration surface review, and a build-versus-buy check against the platform licences you already hold.
Output: A solution definition, data readiness verdict, and costed business case for your approval committee.
Design the system and how it will be judged, together.
Model and hosting selection against residency, latency, and cost constraints. Retrieval and integration design covering write-back and approvals. Golden datasets and acceptance thresholds agreed upfront.
Output: An architecture and evaluation pack your CTO, CISO, and risk function can sign off once.
Build the data layer, then build on top of it.
Pipelines, master data reconciliation, semantic definitions, and retrieval indexing — then the AI system itself, with deterministic guardrails and integration into your systems of record.
Output: Validated pipelines plus a working build, with AI-facing data reconciled to your reporting.
Prove it against real traffic before it gets authority.
Accuracy against acceptance thresholds, adversarial and edge-case testing, integration and reversal-path verification, and cost per transaction measured at projected volume — then shadow-mode running.
Output: A hardened release with evaluation evidence and a shadow-mode comparison against the current process.
Redesign the process, then keep the system honest.
Process redesign, user training on where the system is and is not reliable, and escalation paths — then drift and quality monitoring, controlled model-version migration, and inference cost optimization.
Output: A live system on a quarterly outcome review, with the platform layer reused by use case two.
Why Choose GrowExx for Enterprise AI Development?
We Land AI Inside the System of Record
GrowExx runs a full Oracle practice — ERP, HCM, SCM, CX, analytics, managed services — alongside its AI practice. The hardest part of enterprise AI is safe write-back into a live transactional system. Most AI vendors have never had to do it.
We Run Our Own AI in Production
Recogent for reconciliation, Hirin for recruiting, Inventory Forecasting on Oracle Cloud Infrastructure, Conversational BI, Churn Prediction. These are GrowExx products, where the accuracy and cost problems are ours.
Data Engineering Is in the Same Team
The most common reason enterprise AI misses its business case is data that could not support it. We build the pipelines and master data reconciliation in the same engagement, so that surfaces in week three, not in UAT.
We Tell You When Not to Build
Every discovery checks what Oracle, Microsoft, Salesforce, or ServiceNow already ships inside licences you own. Where buying is the better answer, we say so. Recommending less work is how you earn the harder problems.
Governance Designed In, Not Retrofitted
Golden datasets, acceptance thresholds, decision audit trails, and model inventory are architecture decisions in our method. That is what gets a system through your own approval process on the first attempt.
Model-Neutral and Portable
We do not sell a platform or resell a model. Systems are built through a model gateway with provider abstraction, so switching model or vendor later is a configuration change, not a rebuild.
Convinced? Schedule a 30-min enterprise AI roadmap call →
Real-World Enterprise AI 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
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Recogent — an enterprise AI system built the way this page describes.
Recogent automates account reconciliation across general ledger, AP, AR, intercompany, inventory, and fixed assets. It is the textbook enterprise AI problem: high volume, rules-heavy but exception-rich, bound tightly to the ledger, and unforgiving on both accuracy and audit trail.
Where to go next.
Production-grade GenAI for content, code, and customer experience.
Pre-production audit of AI features for security, compliance, and performance.
