Enterprise Systems

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.

Why this matters

Most established companies carry a 10 to 20-year-old ERP or core business system. It still works — but every small change comes with a vendor invoice, every new feature takes six months, and modern integrations (real-time APIs, mobile, event streams) become impossible. Big-bang replacement projects fail or double their budget about 70% of the time. The strangler-fig pattern leaves the legacy system in place and grows modern services around it, taking over functionality piece by piece. Risk is low, return is early.

Inaction has a cost, and it compounds every quarter. The real price of an aging core system is not the license invoice; the hidden line items are usually worse. Modules that only one developer understands, and that nobody else dares touch, raise your bus-factor risk — when that person leaves, institutional memory leaves with them. A system without a modern API forces every new integration into manual data transfers or fragile nightly batch jobs. Vendor dependence erases your leverage at the negotiating table. And most of all: when competitors ship features in weeks while you measure the same work in months, the gap stops being technical and becomes commercial. The longer you defer, the larger the surface to migrate grows. Work that takes 18 months today can take 30 months three years from now.

What we deliver

Our deliverables are not a software package; they are a transition guarantee. Each item exists to free you from the legacy system without ever stopping operations.

• Scope map and migration sequence. We score every module on two axes: business criticality and technical complexity. This matrix turns the migration order from a subjective debate into a data-backed plan. We start with low-risk, high-frequency work so the team learns the pattern while continuity stays safe.
• Anti-corruption layer. We build a translation and synchronization layer between the legacy system and the modern services. The legacy system’s quirky data model never leaks into the new services; both sides read the same data correctly at the same time. This layer is the technical heart of incremental migration.
• Modern service build. Microservices or a modular monolith on Node.js, Python, or .NET — we decide based on your team size and operational maturity, not on fashion. A PostgreSQL warehouse, an event bus, and contract-based APIs are our defaults.
• User transition management. A software change is also a habit change. For teams moving to the new interface we define training, a parallel-running period, a feedback loop, and a clear “legacy module retires” criterion for each step.

Our approach

We treat modernization as a maturity journey; “delete everything and rewrite from scratch” is rarely a decision and often an escape. To decide what happens to each module, we use the 6R framework: every module is consciously assigned one of six options.

1. Retain. Modules that work, rarely change, and carry low risk stay as they are. Modernizing everything is not a goal.
2. Rehost. The code stays the same; the infrastructure moves to a modern environment (containers, cloud). A fast win with low risk.
3. Replatform. The core logic is kept while its surroundings modernize — for example, the database moves to PostgreSQL and the interface is refreshed.
4. Refactor. The code is cleaned and made modular from the inside; behavior stays identical, but maintenance gets easier.
5. Rebuild. The module is rewritten from scratch as a modern service — only where business value is high and rescuing the old code makes no sense.
6. Replace. The module is swapped for a standard off-the-shelf solution (for example, a managed SaaS for billing) — the most sensible path for functions that create no competitive advantage.

This framework matches the right tool to the right job instead of splitting every module into “renew” or “keep”. On top of it we apply a maturity model: Level 0 (undocumented, single-person dependency, manual deploys) → Level 1 (source control, basic CI) → Level 2 (automated tests, observability) → Level 3 (independently deployable services, contract-based APIs). The goal is not to push every module to Level 3; it is to move each module to the level its business value requires.

Process

• Weeks 1–4 — Inventory and decision matrix. Module map, integration surface, user journeys, and business-criticality analysis of the current system. Output: a migration-sequence document with 6R decisions applied and a risk register.
• Weeks 5–8 — Foundation and first module. The anti-corruption layer is built and the first module (usually a low-risk, high-frequency workflow) goes live. Old and new run in parallel in live A/B mode. Output: a working migration pattern and a rollback procedure.
• Months 3–9 — Incremental migration. Three to five further modules are migrated one at a time. Each ships standalone; rollback stays available at every stage. Output: a deploy, test, and training package per module.
• Months 10–18 — Shutdown. Remaining modules are migrated and the legacy shutdown plan is executed. The vendor contract is terminated only after the final module is safely moved. Output: an independent modern stack and a maintenance handover.

A sample scenario

An omnichannel retail brand was boxed in by the 16-year-old custom ERP at its core: there was no real-time data between the e-commerce site and store stock, and every promotion rule became a development order placed with the vendor. Using the strangler-fig approach, we moved stock synchronization first, then the pricing engine, onto modern services. Six modules were modernized in 14 months. Time to publish a new campaign rule dropped from weeks to a single day, the annual vendor development budget fell noticeably, and operations ran without interruption throughout. Results vary by organization; these figures show the scale of one scenario, not a guarantee.

FAQ

Can this transition really happen with zero downtime? That is the entire point of the strangler-fig pattern. The legacy system keeps running until each module is migrated; new services come online beside it. There is no single “big migration night”, so there is no night on which everything can go wrong.

Why do you avoid a big-bang rewrite? Because the industry data is clear: a significant share of large one-shot rewrites blow their budget or stall halfway. Incremental migration leaves a working system and a reversible decision at every step. Risk becomes manageable once it is split into small pieces.

Can we start while our contract with the current vendor is still active? Yes. We begin most projects while the vendor contract is still running. The anti-corruption layer keeps both systems in sync; you wind the contract down gradually, only after the relevant modules are safely migrated.

Which module should we migrate first? Usually a non-critical but frequently used workflow. This lets the team learn the pattern at low risk and delivers the first concrete win early, which in turn strengthens internal support for the program.

To plan a piece-by-piece modernization of your legacy system inside the enterprise systems pillar, talk to us.

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.

Why this matters

Software written for one customer ships fast — but copying that codebase across 50 customers is operational collapse. A multi-tenant SaaS platform is the architecture where the same codebase serves many customers, each isolated by data, pricing plan, and integrations. Designed correctly, the platform scales exponentially. Designed wrong, and within a year nobody can answer “whose data is this?”. beynart sits on the right side of that line — with architectural decisions that have run in production.

The cost of a wrong start is higher than most founders assume. The most dangerous pattern is the “one copy per customer” model: the code forks with every new deal, and three months later you hold 50 separate versions with no way to ship a common update. When a single security patch has to be tested in 50 places, the team stops shipping features. And when isolation is not built correctly from day one, every query pushes the “which tenant owns this row” check up into the application layer — and one day someone forgets it. A data leak in a SaaS company is not a technical bug; it is an existential crisis. These are among the most expensive decisions to reverse: changing the isolation model with thousands of live tenants is far harder than getting it right at the start. That is why we discuss architecture in the first week.

What we deliver

Our delivery is not a feature list; it is a scalable operating platform. Each component is designed so that growing the product does not slow you down.

• Tenant isolation architecture. We make a reasoned choice between single-database multi-schema, separate-database, or silo models. Encryption, backup, and compliance requirements (GDPR, KVKK, SOC 2) are part of that decision, not a patch bolted on later.
• Authentication and authorization. Tenant-level SSO, role-based access control, audit logs, OAuth and SAML support. Multi-region data residency options where they are required.
• Pricing and subscription engine. Plan management, usage-based billing, trial and grace-period logic on Stripe or Iyzico. A price change becomes a configuration, not a code release.
• Operational tooling. A tenant onboarding console, a billing dashboard, audit-bound support impersonation, and usage analytics. These are the tools your customer success team uses every day; they are in scope from the start, not after launch.

Our approach

Most multi-tenant decisions are conscious trade-offs made along three axes. We do not judge an architecture as “good” or “bad” but as fit for your business context.

The isolation spectrum. There are two extremes, and the right place is usually between them:

• Pooled (single database, single schema, row filtering by tenant ID). Lowest cost, easiest updates, highest density. The right starting point for most B2B SaaS. The risk: isolation depends on discipline.
• Bridge (single database, one schema per tenant). Stronger logical separation, still a single operation. A balanced choice for mid-scale.
• Siloed (a separate database or stack per tenant). Strongest isolation, highest cost. Reserved for enterprise customers with strict compliance needs or data-residency mandates.

The right design is often hybrid: the large majority of tenants in the pooled tier, with only a few big customers in the siloed tier. To make that call, we clarify three questions: compliance obligations (which certifications, which countries), expected tenant distribution (many small or a few large), and noisy-neighbor tolerance. On top of it we set a tiered scale target — we define separate limits for 10-, 1,000-, and 10,000-tenant scenarios up front, because an architecture that is right for 10 tenants can be wrong at 10,000.

Process

• Weeks 1–3 — Architecture workshop. The isolation model, data model, and scale target (10? 1,000? 10,000 tenants?) are clarified. Output: a reasoned architecture-decision document and a compliance map.
• Weeks 4–10 — Core platform. Authentication, tenant lifecycle, billing, and the first business features are built. Output: a working platform that can carry its first tenant.
• Weeks 11–14 — Operational layer. Operational tools, monitoring, runbooks, and the first pilot tenant onboarding. Output: a console where customer success can open a tenant on its own.
• Weeks 15+ — Launch and learn. Production launch and a learning loop with the first five tenants. Output: onboarding and billing flows refined against real usage.

A sample scenario

An HR-tech team had written a product for one customer, then forked the code to sell to a second — and quickly fell into the “every customer is a separate version” trap. We rebuilt it on a hybrid isolation model: the majority of tenants in the pooled tier, large customers with data-residency requirements in a separate region. We reached production in 14 weeks; within six months the platform served dozens of tenants across three countries. The customer success team took new-tenant setup from an hours-long manual task down to a few-minute console flow. Numbers vary by product; this is the scale of one scenario, not a commitment.

FAQ

Does sharing a single database put my tenants at risk? No — when it is built correctly. In the pooled model, isolation is embedded in the database with row-level security policies; the “don’t forget the tenant check” responsibility is not left to individual developers. For customers with strict compliance needs, the hybrid model offers a separate siloed tier.

Can you convert our existing single-customer product to multi-tenant? Yes — it is one of the things we do most often. We first surface the current data model and its hidden single-tenant assumptions, then add the isolation layer incrementally. We run the transition step by step, without an outage for the existing customer.

Why do you include billing from the start? Because billing added later is one of the most expensive rewrites. Plan structure, usage metering, and subscription states are intertwined with the isolation model; designing them together up front is far cheaper than tearing everything out six months in.

We’ll start with 10 tenants; isn’t designing for 10,000 over-engineering? The goal is not to build for 10,000 tenants today; it is to leave that path open. We shape the architecture so the day-one cost stays low but growth never forces a rewrite. A few decisions made early become the most expensive debt later.

To build your multi-tenant SaaS inside the enterprise systems pillar, talk to us.

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.

Why this matters

When the production system goes down and the first answer is “we don’t know where to look”, the problem is not the technology — it’s the observability layer. Logs are scattered, metric names drift between services, there is no distributed trace, and an error reaches the user 40 minutes before anyone notices. An SRE and observability setup closes that gap. The system’s state, the thresholds it crosses, and who responds with which runbook all become explicit. At beynart, this layer is not a tooling choice; it is an organisational contract.

The cost of invisibility grows with every minute an outage drags on. In a B2B SaaS, a long outage covers more than that hour’s revenue: it includes SLA penalties in the contract, trust lost in renewal talks, and a team burning out at midnight. If users call you before the error does, this is no longer an engineering problem but a commercial reputation problem. There is a subtler cost, too: without observability, teams over-provision capacity “just in case”, because they cannot measure what is actually slow. Performance issues get “fixed” by blind guesswork, and every deploy becomes a gamble. Observability removes that blindness — and an SRE practice built without it is an attempt to manage something you cannot measure.

What we deliver

Our delivery is not a monitoring-tool install; it is a reliability practice. The goal is not just to produce charts but to make sure the right person looks in the right place at the right moment.

• Three pillars of observability. Structured logging (JSON, correlation IDs), metrics (Prometheus or the OpenTelemetry collector), and distributed tracing (OTel with Tempo or Jaeger). One dashboard reads request, error, and latency together, so the root cause does not scatter across “log, metric, or trace”.
• SLO and SLA definitions. Availability and latency SLOs for the 5 to 10 business-critical services, with error budgets, and SLA-tied customer reporting where required. Targets are derived from user experience, not from an engineer’s intuition.
• Incident runbooks. Step-by-step response documents for the 8 to 12 most common failure modes — the on-call engineer is never empty-handed. A postmortem template and action-item tracking turn every incident into a lesson.
• On-call rotation. Weekly rotation on PagerDuty or Opsgenie, escalation rules, and a fatigue-aware cadence. A sustainable on-call experience matters as much as well-built alerts.

Our approach

We treat observability not as a tool install but as a maturity model. Most teams get stuck at Level 0 and try to leap straight to the top — which produces a setup that generates noise but no confidence. We move through four levels:

• Level 0 — Reactive. Users complain, the team greps log files by hand. No visibility, MTTR is unpredictable.
• Level 1 — Centralized. Logs and metrics land in one place; basic dashboards exist. “Where do we look” now has an answer.
• Level 2 — Proactive. Distributed tracing is in place; alerts are tied to user impact, not to raw thresholds. The system tells you before you look.
• Level 3 — Predictive. SLOs and error budgets drive decisions; when the budget is spent, feature work pauses and reliability takes priority. Observability becomes a management decision, not just engineering.

At the center of these decisions sit the four golden signals — latency, traffic, error rate, and saturation. Instead of drowning in hundreds of metrics, measuring these four correctly for every critical service points most of your money and attention at the right place. Our alerting philosophy follows from this: an alert should fire only if a human needs to get up at midnight and act. Every “interesting but not actionable” notification is noise that drowns the real alert.

Process

• Weeks 1–2 — Inventory. Service inventory, current state of logs and metrics, and a map of critical user journeys. Output: a maturity-level assessment and a prioritized service list.
• Weeks 3–4 — Instrumentation. OpenTelemetry setup, a structured log format, correlation IDs, and first dashboards. Output: baseline visibility where the three pillars meet on one dashboard.
• Weeks 5–6 — SLOs and alerts. SLO definitions, an error-budget ledger, and alert thresholds tied to user impact. Output: a noise-free, actionable alert set.
• Weeks 7–8 — Practice. Runbook authoring, on-call rotation kick-off, and a first gameday (controlled-failure) exercise. Output: a rehearsed incident-response reflex for the team.

A sample scenario

A B2B SaaS team learned about almost every outage from a customer call; logs lived in three different places, and alerts had become a wall of noise everyone ignored (close to Level 0). In eight weeks we gathered the three pillars onto one dashboard, tied the four golden signals to the critical services, and rewrote alerts around user impact. Mean time to recover dropped from hours to minutes; a meaningful share of production errors are now caught by alerts before users see them. Perhaps the most lasting gain was a clear reduction in on-call fatigue — the team now knows where to look when an alert fires. These figures are from one scenario; results vary by stack.

FAQ

What is the difference between observability and monitoring? Monitoring asks questions you already know: “Did CPU cross 90%?” Observability lets you ask questions you did not anticipate: “Is this new error happening only for one tenant, on one endpoint, and only since the last deploy?” When the three pillars are built together, you can chase down a failure you have never seen before.

Are SLOs and SLAs the same thing? No. An SLA is the external promise you make to customers, usually with a penalty clause. An SLO is the stricter internal target your team sets for itself. The buffer between them is your error budget: it measures how much risk you can take before breaching the SLO and turns the balance between feature speed and reliability into a number.

We get too many alerts and the team has stopped looking. What now? That is the classic symptom of alert fatigue, and the cure is not more alerts but fewer, better ones. Our rule is simple: an alert fires only if it needs a human to act immediately. We move non-actionable notifications to a dashboard and reserve paging for real incidents.

We’re a small team; is a full SRE practice too much for us? For a small team we do not build the whole stack; we target the level of the maturity model that will not overwhelm you. For most small teams, Level 1–2 is the right spot: centralized visibility and a few alerts that genuinely matter. SRE is a discipline, not a headcount; even a one-person team can practice it.

To adapt observability and SRE practice to your stack inside the enterprise systems pillar, talk to us.