Layer 04 / 04
ERP to custom software, the foundation of AI-native operations.
We build the invisible side of the brand: ERP integration, master data model, KVKK/GDPR compliance, and custom software. We embed AI into the workflow, not just the interface.
Every investment in AI returns in proportion to how clean and connected the underlying data is. Fragmented ERPs, disconnected CRM and finance systems, brands that have grown on separate stacks: in all of these, the cost of making a decision quietly compounds: manual reconciliation, duplicate data entry, different numbers across departments. At beynart, we treat enterprise system integration not as cosmetic modernization but as the foundation that AI-native operations run on.
Single source of truth. Clean master data. Compliance built in from day one.
Trying to add an analytics or AI layer on top of fragmented systems is like adding floors to a building with a cracked foundation. The visible part might look fine; when the load increases, the bill arrives. That’s why we insist on getting enterprise systems right from the start: when ERP integration, master data model, and KVKK/GDPR compliance are done correctly the first time, every capability that follows (reporting, automation, AI) grows several times faster on that foundation.
If you don’t have a document that explains which system owns what, that’s the question we start with.
Enterprise systems are the layer where an organization's accumulated knowledge sits inside software. Off-the-shelf packages like SAP, Logo, or Microsoft Dynamics give a fast start; but as the business matures, vendor lock-in, license cost, and inflexibility surface. At beynart, the goal is rarely a full ERP replacement; it's custom software and integration at the right points, fitted to how the business actually runs. The five arguments below repeat across every enterprise engagement we've run in the past four years.
25-40% faster operational decisions
When ERP, CRM, e-commerce, and accounting run in silos, every department sees a different version of the same customer. McKinsey research shows integrated data infrastructure lets companies decide 25-40% faster and reclaims 300-900 hours of annual reconciliation. In our engagements, post-integration order-confirmation time drops from four days to four hours; finance close moves three days earlier. Measured outcomes, not projections.
60% analytics value lost in data silos (Gartner)
Customer, product, and supplier records living in separate systems drift and contradict each other over time. Without a single master data layer, every investment in AI or analytics tools is forced to work with incomplete information. Gartner data puts this plainly: 60% of organizations fail to extract full value from analytics investments because of data-quality issues. A proper master data model closes that gap: every system sees the same customer ID, the same product code, the same figure.
3-5× cost difference: built-in vs retrofit
Turkish KVKK Board administrative fines averaged 250,000 TRY in 2023-2024; EU GDPR violations can reach 4% of global revenue. But the larger cost is never the fine; it's the customer trust lost and brand damage that follows an incident. Embedding personal- data inventory, retention policy, breach detection, and audit trails at the architecture phase requires 3-5× fewer resources than a post-launch compliance retrofit and eliminates the violation before it happens.
60-70% faster new-brand onboarding
When multiple brands or sub-products within a holding company grow on separate systems, consolidation cost compounds with every new launch. The right architecture (a multi-tenant data layer, centralized identity management, isolated-but-shared services) cuts each new brand's onboarding time by 60-70%. beynart builds the technical backbone of Hub & Spoke product strategy on this principle; caveflo.com and crm2b.com share the same core service layer with independent data isolation.
30-50% license cost reduction
A gradual exit from the closed ecosystems of SAP, Logo, and NetSuite is achievable. When critical business logic moves into TypeScript or Python services, the ERP stays only as the system of record; license costs drop 30-50%. More important: business processes captured in software become organizational memory independent of staff turnover. Knowledge moved from people into code is the most valuable and least-appreciated asset in operations.
in this layer
01
We connect SAP, Logo, Microsoft Dynamics, and Odoo to your CRM, e-commerce, and marketing stack. A master data model that becomes the single source of truth makes every report consistent and eliminates duplicate data entry.
02
For local regulation, sector-specific process, and customer-bound flows that off-the-shelf SaaS can't cover: web/mobile applications, APIs, and internal tools. Business logic moves into TypeScript or Python services; the ERP stays as the system of record.
03
Personal-data inventory, retention policy, breach detection, and audit trails. Compliance baseline is embedded at the architecture phase; bolting it on later costs 3-5× more. Audit-ready infrastructure means you answer a regulatory inspection without scrambling.
04
Kubernetes + Postgres + Cloudflare ready for 10x-100x traffic growth. CI/CD pipeline, Sentry + Grafana + Loki observability, and SRE discipline handed back to your team with full runbooks.
go deeper
use case
Strangler-fig migration of legacy ERP: gradual replacement, zero downtime.
Instead of a single big-bang rewrite, we wrap the legacy system with modern services and migrate functionality piece by piece. Vendor lock-in can be exited within 18 months.
deliverable
One codebase, many customers: a full-stack production build.
We build your B2B SaaS product on a multi-tenant architecture, from scratch or by migrating an existing single-tenant implementation. Tenant isolation, pricing plans, and operational tools are all in scope.
deliverable
Logs, metrics, traces and SLOs: know what the system is doing and when to step in.
We build the observability layer that explains why your production systems slow down or fail. OpenTelemetry, structured logging, SLO definitions, and incident runbooks come together as one practice.
how we work
Building enterprise software follows a five-phase discipline: discovery → architecture → integration → enablement → operations. Each phase ships a concrete output; each transition starts with leadership sign-off. Skipping a phase looks faster at the start; it comes back as architectural debt that compounds.
01
We map the existing ERP, integration topology, user roles, data flows, and compliance requirements. Every business process is evaluated with a TCO + flexibility analysis to inform the build vs buy decision. The resulting picture is presented for leadership sign-off; scope is locked before the next phase begins.
02
Service boundaries, master data model, API contracts, and cloud topology are defined. Postgres / Kubernetes / Cloudflare choices are documented. Security baseline and KVKK/GDPR requirements are embedded into the architecture, not added later. C4 model diagrams become the shared language between engineers and the business.
03
The most critical 1-2 modules taken end-to-end into production. Bridge services connecting the existing ERP, CRM, and e-commerce platform are tested with real users. Their feedback shapes the next sprint's scope. CI/CD and the observability stack go live before any module reaches production.
04
Data and processes move from the legacy system using dual-write and gradual cutover to de-risk the transition. A rollback plan stays ready throughout. Parallel user training and internal documentation mean your team can operate the new system without asking beynart for instructions.
05
24/7 monitoring, on-call rotation, and incident response. Uptime, latency, and error-rate are reported against SLAs monthly. Post-mortem discipline produces durable improvements, not temporary patches. As the system grows (new brand, new module, new user base) the architecture is reviewed quarterly.
frequently asked
by the numbers
Zero
Downtime migration
ERP+
Integration
100%
Compliance ready
Data-mesh
Model
Let's find the layer that fits your need and map where the architecture begins.
We bring the systems and the products already built for it, ready to run, not just to pitch.
