Your ERP, CRM, and legacy systems already hold everything they need to think for themselves — they just need AI wired in. We integrate intelligence into what you already run, without the disruption of starting over.
Stratify Agent
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Hi! I am the Stratify AI Integration Agent. Ask me anything about how we can securely connect LLMs and autonomous agents to SAP, Dynamics, Odoo, or legacy SQL databases.
Enterprise AI integration connects the systems you already run — ERP, CRM, databases, document stores — to AI models that can read that data and act on it. The systems stay where they are. What changes is that operational data becomes something a model can reason over, and a model's output becomes something your systems accept.
It is the alternative to the two approaches enterprises usually default to: replacing a working system because the vendor's AI add-on is the only route to AI, or wiring point-to-point scripts between tools until nobody can safely change anything. Integration keeps the system of record intact and adds an AI layer above it, connected through interfaces that survive the underlying software being upgraded.
We put AI into SAP, Dynamics, Salesforce, Odoo and the legacy databases underneath them — under your own keys, inside your own boundary.
No rip-and-replace. We connect AI to the ERP and CRM your organisation already runs on, through APIs or direct database access where no API exists.
Vendor AI add-ons like Einstein or Copilot Studio bill per user, every month, forever. A one-time integration avoids that entirely.
Models run under your own API keys. Where data cannot leave your infrastructure, we deploy open-weight models inside a boundary you control.
The middleware source code and IP are handed over, so your own engineers can extend the integration without coming back to us for every change.
We build custom middleware, APIs, and AI connectors to add cognitive capabilities directly into your legacy stacks.
Add lead scoring, auto-follow-up, and conversational agents to your existing Salesforce, Dynamics, or Odoo CRM.
Connect predictive analytics and natural-language reporting to your existing ERP data.
Layer AI screening, onboarding assistants, and policy Q&A onto your current HRIS.
Connect PDT/handheld devices, production systems, and middleware (e.g., Celery-based queues) to AI-driven monitoring.
Bring even decades-old systems into the AI era via RAG pipelines and secure API bridges.
Any system, any stack — if it has data, we can make it intelligent.
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Start small, scale effortlessly
We build with you, not for you. See the solution early, shape it together,
and scale with confidence.
See it before we build it.
We rapidly build an interactive AI-powered app preview so you can experience the solution early.
Your business shapes it.
We document your processes, gather feedback, and refine the solution to fit your needs.
Built strong. Built secure. Built to scale.
We turn the preview into a robust, enterprise-grade application with security and integrations.
Deploy without disruption.
We roll out in controlled phases with testing, migration, and training for a smooth launch.
Keep improving. Stay ahead.
We continuously enhance the solution with new AI capabilities as your business grows.
Adding AI to an enterprise estate comes down to three structurally different routes. The distinctions below are architectural, not performance claims.
| Comparison criterion | AI integration layer | Rip-and-replace platform | Point-to-point scripts |
|---|---|---|---|
| System of record | Stays as-is. The AI layer reads and writes through defined interfaces. | Replaced, along with the migration and retraining that implies. | Stays, but accumulates undocumented dependencies over time. |
| Data movement | Data stays in its source system; the layer queries it in place. | Full migration, with the reconciliation risk that carries. | Copies proliferate across scripts, and drift between them. |
| Who maintains it | You do — the middleware source code is handed over. | The platform vendor, on their release cycle. | Whoever wrote the script, if they are still available. |
| When a schema changes | The interface absorbs it; the change is versioned and tested. | Vendor-managed, but your customisations may not survive it. | Scripts fail silently until somebody notices the bad data. |
| Audit trail | Every AI-initiated read and write is logged against the source record. | Whatever the platform chooses to expose. | Usually none beyond application logs. |
| Rolling back | Disable the layer; the underlying systems are untouched and still running. | A second migration project. | Depends which scripts have become load-bearing. |
| AI model choice | Model-agnostic — swap providers, or run open-weight models on-premise. | Bound to whichever model the platform vendor embedded. | Per-script, so the estate ends up with several inconsistent choices. |
These describe how each approach is structured rather than measured outcomes. Which fits depends on the age of your systems, your data residency obligations and how much change your operations can absorb at once.
One development fee to build it. Then the only ongoing cost is the model subscription you hold directly with the provider, on your own card, scaled to how much work gets done. Nothing recurring comes to us at all.
A one-time development fee, scoped to the work in question and fixed before we start. At handover the source code, the logic and the IP are yours to keep, extend or take elsewhere.
Your integration needs model capacity to run. You hold that subscription directly with the model provider and pay them from your own card — it never passes through us, so there is nothing for us to resell or mark up. What it costs depends on how much work gets done, not on how many people you have.
There is no support contract and no retainer to sign. It keeps running on its own, and you hold the code. When you want it improved, extended or pointed at something new, you come back to us and we quote that piece of work like any other.
Where the development number lands depends on how many systems are involved and how clean the access to them is. Describe what you need and we will scope it properly — including an estimate of what the model subscription will cost you at your volumes — rather than quote a range that means nothing.
Get a scoped estimateThree integration problems we see repeatedly across GCC enterprises, and the shape of the approach in each.
Illustrative scenarios. These are composite examples showing how this service is typically applied. They do not describe specific client engagements and are not case studies.
Paper
Topic
An automated operations dashboard for a manufacturer pulls live data from production systems, the ERP and plant-floor equipment into a single view, and pairs it with alerting that notifies a named person when a metric crosses a threshold that matters. The distinguishing feature is not the charts — it is that the system decides what deserves attention, rather than leaving that judgement to whoever happens to be looking at the screen.
No. Our integration services are designed to connect custom AI engines directly to your existing systems (such as Odoo, SAP, Salesforce, or custom SQL databases) via secure APIs and custom middleware, preserving your existing infrastructure investments.
No. We build and thoroughly test all integration middleware in staging environments and run them in parallel before full deployment to ensure zero downtime and zero disruption to your daily operations.
Yes. We regularly build secure API bridges and custom database connectors to integrate advanced LLMs and AI agents with legacy platforms including SAP, Dynamics, Salesforce, and custom-built enterprise databases.
Most integration projects go live within 3–6 weeks, depending on the complexity of the databases, API endpoints, and the specific automation logic required.
Yes, custom integration is highly cost-effective. A one-time integration setup avoids the recurring monthly per-user license fees of vendor AI add-ons (like Salesforce Einstein or Copilot Studio) and lets you run models under your own API keys.
Through SAP's own interfaces rather than around them — OData services, BAPI/RFC calls, or a read replica where direct access is restricted. The AI layer sits outside SAP and treats it as a system of record it queries and writes back to, which means the integration survives SAP upgrades and does not require custom ABAP that your SAP partner then has to maintain.
The integration talks to versioned interfaces, not to internal tables or screen positions, which is what keeps an upgrade from breaking it. Where a vendor changes an interface between major versions, the middleware is the single place that needs updating rather than every script that touched the system. Because you hold the middleware source code, that update is not gated on our availability.
Access is scoped per integration through service accounts you provision, so the AI layer sees only the records the task requires. Pipelines are encrypted in transit, access is role-based, and every AI-initiated read and write is logged against the source record. Where data cannot leave your estate at all, we run open-weight models inside your own infrastructure so no query crosses the boundary.
Usually yes, though the route matters. Direct database access against a read replica is the cleanest option where the schema is stable. Where it is not, we work through file-based exchange, message queues, or a thin service layer built over the system. What we avoid is screen-scraping the user interface, because that produces an integration that breaks on the next cosmetic change.
Yes. The middleware source code and IP are handed over as part of the engagement, in the same way as our application work. There is no runtime licence to us and no component that stops working if the relationship ends. Practically, that also means your own engineers can extend the integration without coming back to us for every change.
Have a project in mind or need expert guidance?
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