What we can build for you
These are not invented case studies. They are engineering patterns we have implemented, documented, or productized, each linked to something you can inspect: a guide, a product, or working code.
What a solution pattern is, and when to use one
A solution pattern is a reusable architecture we have already built, documented, or productized, ready to adapt to your problem instead of designed from a blank page. Choose a pattern when your problem rhymes with one of these: document automation, governed RAG, AI workflows, product data sync, multi-tenant SaaS, or commerce and ERP integration. The pattern decides the shape, audit trail, and failure handling up front, so the engagement spends its budget on what is specific to you. Choose a bespoke build when the core mechanic has no established pattern. Most enterprise work is a known pattern plus your domain, not a brand-new invention.
- A pattern is proven structure: extraction, access control, idempotency, and audit decided before the project starts, not discovered mid-build.
- It compresses cost and risk because the hard architecture is settled, leaving budget for your domain and edge cases.
- Use a pattern when your problem matches one of the six; use a bespoke build only when the core mechanic is genuinely new.
- Every pattern here links to something you can inspect: a guide, open-source code, or a feature running in production, not an invented case study.
Document automation with human review
Intake, extraction, classification and routing of documents with approval gates and a full audit trail. The pattern behind most production AI value.
Every company we meet has the same pile: invoices, delivery notes, supplier confirmations, claims. Someone opens each document, reads it, retypes the numbers into a system and forwards it. That person is expensive, bored and occasionally wrong. The fix is not a chatbot. It is a pipeline that reads, classifies and routes documents automatically, and pulls a human in exactly when confidence drops.
When you need it
- Your team retypes data from PDFs or emails into ERP, accounting or ticketing systems
- Volume grows faster than headcount and errors surface weeks later
- Compliance requires proof of who approved what and when
How we build it
- 1Map the document types, volumes and the cost of today's manual handling
- 2Build extraction and classification with confidence thresholds; low confidence routes to a human review queue
- 3Wire approval gates, the audit trail and the handover into your target systems
What you get
- A working pipeline in your infrastructure
- An audit trail that satisfies compliance reviews
- A documented runbook your team operates
Pattern architecture: Inbox / Upload / API, Extraction, Classification, Confidence, high, Auto-route, low, Human review, Target system, Audit trail
Governed enterprise RAG
Retrieval over your knowledge with access control, grounding, citations and evaluation. Built so legal and security sign off, not just the demo audience.
The answer exists. It sits in a wiki page from 2022, a closed ticket and the head of support's memory. New employees ask the same questions, customers wait while someone searches. Retrieval-augmented generation makes that knowledge answerable, but only when access control and citations are engineered in from day one, not bolted on after the demo.
When you need it
- Support, sales or engineering keep answering the same questions from scattered sources
- Documentation exists but nobody finds it when it matters
- A generic chatbot already invented answers with full confidence
How we build it
- 1Inventory the knowledge sources and who is allowed to see what
- 2Build the retrieval pipeline: chunking, embeddings, access-controlled retrieval with citations
- 3Stand up the evaluation loop so answer quality is measured, not assumed
What you get
- Grounded answers with citations, not confident guesses
- Access control your legal team signs off
- An evaluation dashboard with real quality numbers
Pattern architecture: Documents / Wikis / Tickets, Chunking + Embeddings, Vector store, User question, Retriever + Access control, LLM with citations, Grounded answer, Evaluation loop
AI-assisted operational workflows
Agents inside real processes: qualification, summarization, handover to humans. The assistant on this site is this pattern, in production.
An agent is software that decides the next step itself, inside boundaries you define. Done badly, it is a demo that breaks in week two. Done properly, it qualifies a lead at 2 am, books the meeting, writes the summary and knows exactly when to stop and hand over to a human. The difference is engineering: explicit tools, logged decisions, hard limits.
When you need it
- A workflow burns hours on triage, qualification or scheduling that follows learnable rules
- Response time decides whether you win the deal or the case
- You want automation but legal needs every automated decision traceable
How we build it
- 1Pick one workflow where minutes matter and failure is visible
- 2Build the agent with explicit tools, qualification logic and a hard handover line to humans
- 3Instrument everything: every action logged, every decision traceable
What you get
- An agent in production, not a demo
- A full decision log for every automated step
- Clean handover points where humans stay in charge
Pattern architecture: Trigger: form / email / event, Agent, Qualify + Summarize, Action, tool, CRM / Calendar / Email, handover, Human, Audit log
Product data synchronization
ERP, PIM, shop and marketplaces kept consistent through idempotent, monitored pipelines. Our open-source Data Hub plugin encodes the discipline.
Product data lives in the ERP, gets enriched in the PIM and has to land correct in the shop and on every marketplace, in every language, at every price change. Manual exports and one-off scripts hold this together until they don't. The pattern: idempotent pipelines that re-run safely, preview before they write and tell you what changed.
When you need it
- Channel data drifts: prices or stock differ between shop, marketplace and ERP
- Imports break when a supplier changes a column and nobody notices for days
- Launching a new channel or language takes weeks of manual mapping
How we build it
- 1Model the data flows between ERP, PIM, shop and marketplaces, including the failure cases
- 2Build idempotent pipelines with dry-run previews, retries and monitoring
- 3Cut over feed by feed, with the old path alive until each new one is proven
What you get
- Consistent data across every channel
- Pipelines that survive supplier format changes
- Monitoring that knows before your customers do
Pattern architecture: ERP, Pipeline: extract - transform - load, PIM, Shop, Marketplace feeds, Monitoring + Retries
Multi-tenant SaaS platforms
Tenant isolation, billing, role models and operational tooling engineered from day one instead of bolted on after the tenth customer.
The first customer of your platform is easy. The tenth exposes every shortcut: shared data leaking across accounts, billing logic in spreadsheets, deployments everyone fears. Multi-tenancy is an architecture decision, and retrofitting it costs a multiple of building it right. We build the spine first: isolation, roles, billing, operations.
When you need it
- You are turning an internal tool or a single-client build into a product
- Tenant onboarding still means manual database or deployment work
- Investors or enterprise buyers ask how data isolation actually works
How we build it
- 1Design tenant isolation, the role model and billing as architecture, not afterthoughts
- 2Build the platform spine: gateway, tenant context, isolated data, ops tooling
- 3Harden for the second and the tenth tenant: migrations, limits, observability
What you get
- A platform that onboards tenants in hours
- Cost and usage transparency per tenant
- Documentation that makes tenant two cheaper than tenant one
Pattern architecture: Tenant A, Gateway + Tenant context, Tenant B, Services, Isolated data per tenant, Billing + Roles + Ops tooling
Commerce and ERP integration
Order sync, inventory, pricing and invoicing flows between shop and ERP that survive retries, partial failures and month-end load.
An order placed at 23:58 on the last day of the month, paid by invoice, with one item out of stock: this is where shop-ERP integrations die. The happy path is easy; the partial failures are the project. We build the integration layer with idempotency, dead-letter queues and business-level alerting, so the order either completes or someone learns exactly why not.
When you need it
- Orders go missing or double between shop and ERP and nobody can say where
- Stock and prices lag, so you oversell or undersell
- Month-end volume turns the integration into a support fire drill
How we build it
- 1Map order, stock, price and invoice flows including partial-failure behavior
- 2Build the integration layer with idempotency, dead-letter queues and alerting
- 3Load-test against month-end volume before anyone depends on it
What you get
- Order flows that survive retries and month-end load
- One integration layer instead of point-to-point spaghetti
- Alerting on the business level: which order failed, not which pod died
Pattern architecture: Shop, orders, Integration layer, idempotent, ERP, stock + prices, invoices, Customer, Dead-letter + Alerting
Who you're working with
Engagement levels
Oronts works with serious teams that need senior delivery, not low-cost outsourcing.
- Production Pilot
- from 25k EUR
- Custom software and AI projects
- from 50k EUR
- Ongoing technical retainers
- from 15k EUR/month
Exact pricing depends on scope, responsibility, delivery speed, team size, integrations, support expectations and production risk.
Solution patterns: frequently asked questions
Your case is not on this list?
The patterns cover what repeats; engagements cover what is specific. Describe your problem and a senior engineer maps it.