AI & Machine Learning

We develop AI solutions that genuinely help in everyday work: from LLM assistants and semantic search to forecasting and automation. Always with a clear target vision, measurable quality, and clean integration into your systems—so AI doesn’t stay a “demo,” but becomes a product.

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PoC

Validate quickly, plan properly

RAG

Make knowledge usable

MLOps

Monitoring & quality in production

AI & Machine Learning: DevSolux develops LLM assistants, RAG and ML models for measurable value

From idea to proof of value

Data protection & guardrails

What we enable for you with AI

AI delivers real value when it’s embedded in processes: a solid data foundation, sensible user flows, clean integrations, and measurable quality. We build solutions that take pressure off teams, make knowledge accessible, and support decision-making—without the “black box” feeling.

LLM assistants & chatbots

Assistants for support, sales, or internal teams—with clear roles, safe answer rules, and traceable sources. Ideal when information is scattered and fast answers matter.

Semantic search & RAG

Knowledge systems based on embeddings and vector search: documents, tickets, Confluence, PDFs, or wikis become searchable—including Retrieval-Augmented Generation (RAG) for precise, context-based answers.

Document processing & NLP

Classification, extraction, and summarization of texts—e.g., invoices, contracts, emails, or reports. Goal: less manual data entry, better data quality, and faster processing.

Forecasting & predictive analytics

Demand, revenue, or capacity forecasts, anomaly detection, and scoring models. For better planning, fewer surprises, and data-driven decisions.

Computer vision

Image recognition for quality, documents, or visual inspections—e.g., defect detection, classification, or structured analysis of image data. Pragmatically implemented to fit the use case.

MLOps, monitoring & operations

Deployment, evaluations, drift detection, logging, and feedback loops. So models & assistants remain reliable long-term—including updates, versioning, and rollbacks.

How AI goes from experiment to product

We start with value and data reality: Which decisions, texts, or workflows should improve? Then we build a solution with clear evaluation, guardrails, and integration into your systems. This keeps AI explainable, safe, and measurable in production.

AI system with RAG, evaluation and monitoring

Embeddings Evaluation Guardrails

Discovery & data readiness

Clarify use cases, user roles, risks, and data sources. Result: a clear scope, success criteria (KPIs), and a solid recommendation on whether RAG, classic ML, or both are a fit.

Solution design & evaluation

Define architecture, data model, prompts/policies, retrieval strategy, and test set. Quality is made measurable (e.g., precision, hallucination rate, faithfulness, latency).

Implementation & integration

Iterative delivery: data pipelines, vector index, model/LLM connectivity, APIs, and UI integration. Includes guardrails, logging, and clear error handling—so the system stays robust.

Go live & MLOps

Controlled rollout, monitoring, feedback loops, and continuous improvements. This keeps AI reliable—even when data, processes, or requirements change.

Technology & standards

DevSolux implements AI in a way that stays responsible and maintainable: data quality, clear policies, security & data protection, and measurable evaluation are standard. We choose models and architecture based on your use case—not buzzwords.

LLM & RAG: embeddings, vector search, source citations, prompt/policy design
Evaluation: test sets, quality metrics, regression checks, human-in-the-loop
Security & GDPR: data minimization, access control, secrets, auditability
MLOps: versioning, monitoring, drift detection, feedback & continuous improvement

Semantic Search RAG MLOps

Questions that almost always come up at the beginning

For knowledge questions, text work, and assistant use cases, LLMs with RAG are often ideal. For predictions, scoring, anomalies, or structured optimization, classic ML is often better. In many projects, the best solution is a combination—depending on data, risk, and target vision.

With clear guardrails, source citations (RAG), evaluation via test sets, and regular regression checks. We also build in feedback loops and monitoring so quality is visible in production—and not only in a demo.

Yes—if it’s planned properly. We focus on data minimization, access control, secure secrets, audit logs, and clear data flows. Depending on requirements, we choose suitable operating models (e.g., isolated environments, controlled indexing, role-based retrieval access).

Through clean APIs, webhooks, and UI embedding—e.g., into web apps, portals, admin back offices, or mobile apps. What matters is the process: who uses it when, what approvals apply, what data may be processed, and how output is checked (human-in-the-loop, if needed).

A short description of the use case (problem, users, expected benefit), relevant data sources (e.g., documents, tickets, CRM, logs), quality requirements, and any compliance constraints. Then we propose a clear next step—often a discovery/proof-of-value with measurable criteria.

Ready for AI that delivers measurable value—instead of just impressing?

Briefly describe your use case, your data sources, and what “good” concretely means. We’ll get back to you with a clear, actionable next step—including a proof-of-value recommendation.

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