AI and business process automation: agents, LLMs and workflows
Artificial intelligence goes into flows, not slides. We combine software development and LLM expertise to bring AI into real processes: company knowledge, document automation, classification, operational agents connected to CRM, ERP, APIs and databases. With governance, security and measured quality.
Company knowledge, documents, CRM, ERP, tickets
LLM, agents, classification, semantic search, rules
Responses, actions, summaries, alerts, dashboard, content
DigiFluss brings AI into business processes: searchable knowledge bases (RAG), departmental assistants, automatic classification of emails, tickets and documents, operational agents connected to CRM, ERP and APIs. With governance, human control over critical actions and measured quality.
We don't sell AI. We sell problems solved with AI.
One of the two founders is a computer engineer specialized in LLMs and software development. Our AI skills are not a repackaging of other people's tools: we design, build and ship complete LLM systems, integrated with real business data.
AI makes sense when it enters a process that already exists and improves its quality, speed or scalability. Not when it's an "innovative" section of the website or a trade-show demo.
Seven ways we bring AI into your processes.
For each one we show when it makes sense to call us, what we build, the expected result and the concrete examples we've already tackled.
01 Operational automation
The team wastes time on manual handoffs between software: copy-pasting data, generating documents, notifications, recurring reports, checks that depend on someone's memory.
Automated workflows between business tools, event-based triggers, syncs, document generation, threshold alerts, recurring routines. All monitorable and reversible.
More time for the work that creates value. Fewer errors from manual intervention, faster and more traceable processes.
- Automatic quote and contract generation from CRM
- Nightly syncs between CRM, ERP and marketing stack
- Alerts on critical KPIs when they cross defined thresholds
- Customer onboarding workflows automated end-to-end
02 Knowledge base AI
Company knowledge is scattered across documents, drives, emails and people's heads. Finding the right answer takes more and more time and more and more people.
Knowledge bases queryable in natural language, based on RAG (retrieval augmented generation). We index documents, procedures, contracts, manuals and structured data. The team or the customers ask questions, the AI answers citing the sources.
Company knowledge always accessible, always up to date, always verifiable. Consistent and traceable answers, not improvised ones.
- KB AI for technical support with access to manuals and historical ticket
- Internal knowledge base for new employee onboarding
- Assistant for the sales team with catalog, price lists, case studies
- Customer KB integrated into a portal or chat with source citation
03 Internal assistants
Teams need a copilot that speaks their language, knows the company data and speeds up routine tasks. A generic chat isn't enough.
Assistants dedicated to each team (sales, customer care, HR, marketing, back office) with access to the relevant company data, curated prompts, differentiated permissions and interaction logging.
Every team gains speed on repetitive thinking tasks. Your people stop redoing from scratch what the system already knows how to do.
- Sales copilot with access to CRM, catalogue, price lists and materials
- HR assistant for company policies, leave, onboarding
- Marketing support for drafting briefs, product descriptions, content
- Back office assistant for searching contracts, regulations, procedures
04 AI customer care
Incoming requests keep growing, but the team doesn't scale at the same pace. Repetitive replies eat up the time, complex requests get lost in the queue.
AI built into customer care: automatic triage of requests, assisted replies based on internal knowledge, conversation summaries, smart escalations, sentiment analysis. The agent supervises, they don't start over.
Faster first response, more consistent quality, agents who handle the cases that truly need their judgement.
- Automatic triage of emails and tickets with classification and routing
- Contextual suggested replies, based on KB and previous tickets
- Automatic summary of long conversations for handover
- Alerts on critical cases (urgency, negative sentiment, customer escalation)
05 Classification of emails, tickets, leads, documents
Every day brings in emails, leads, tickets and documents that need manual sorting. Often they get misclassified, lost or duplicated.
LLM-based classifiers that read the content, extract metadata and route it automatically to the right team, process or record. With confidence rules, human fallbacks and auditing.
Accurate, fast sorting. Leads land on the right sales rep, tickets on the right team, documents in the right folder. No manual intervention.
- Lead scoring and automatic routing into the sales pipeline
- Sorting shared inbox emails by area and priority
- Classification of contractual documents with field extraction
- Ticket routing by priority, language, product, sentiment
06 Operational agents (CRM, ERP, API, database)
You want to move past the "chatbot that answers" stage: you need an AI that acts, not just one that talks. Performing actions on CRM, ERP and management systems, with clear rules and responsibilities.
LLM agents connected to the company systems through API and tool calling. They can read, reason, decide and carry out controlled actions (create records, update master data, send communications, generate reports).
A new operational capability, not a showcase. The AI stops being a consultant and becomes an executor with rules.
- An agent that qualifies and enriches leads in the CRM from external sources
- An agent that generates orders in the ERP from natural-language requests
- An agent that runs searches and actions on company databases
- A support agent that opens/updates tickets with proper authorisations
07 Governance, security and quality control
Bringing AI into the company without governance means accepting unmeasured risks: sensitive data fed to the model, unverifiable outputs, decisions with no traceability.
An LLM governance layer: permission management and data segregation, curated and versioned prompts, interaction logging, quality evaluations, human fallback, audit trail. All designed from the start, not bolted-on.
Reliable AI in production, not just in a demo. Traceable decisions, measured quality, protected data, people in control where it matters.
- Role-based data access policies (RBAC) for every assistant
- Automatic evaluation of responses against a dataset of golden answers
- Full logging: input, output, cited sources, decisions, timings
- Human approval points on critical actions (and before any write)
How we decide where AI truly makes sense.
Four criteria we apply to every use case, before recommending an investment in AI.
There's an already defined process. AI accelerates or improves an existing flow. It doesn't invent it. If the process isn't there, it has to be designed first.
There's data to reason on. AI needs real context (documents, CRM, ERP, logs) to generate value. Without data, they stay demos.
It's measurable. We define up front what "it works" means: time saved, accuracy, cost per interaction, satisfaction.
It has a human fallback where needed. Critical cases always have an approval or override point. AI doesn't decide on its own where it carries weight.
Six questions we're almost always asked before we start.
How "AI" does a project need to be for it to make sense to call you?
There's no threshold. We work both on single AI interventions (a classifier, a team assistant, an agent connected to the CRM) and on AI systems that live inside a broader flow. The right question isn't "how much AI?", but "where can it remove friction or raise quality?". We figure that out together during analysis.
Do you use ChatGPT? Claude? Open models?
We choose the model based on the use case, the required security level, the budget and the data involved. We work with OpenAI, Anthropic, Google, Mistral, open source models via Ollama or vLLM, or hybrid stacks. The model choice is a design decision, not an ideological one.
Does my company data end up fed to the model?
No, not by default. We work with configurations that exclude your data from training ("no-training" mode available on all enterprise providers), or with self-hosted models where the data never leaves your infrastructure. The choice depends on the sensitivity level: we address it explicitly during the architecture phase.
How do you measure the quality of an AI response?
We build evaluation datasets ("golden answers") representative of the real use case. We run periodic evaluations, manual for critical cases and automatic for regression. We track metrics such as accuracy, hallucination rate, latency, cost per interaction. We put them in a dedicated dashboard.
What happens if the AI gets a critical action wrong?
We design human approval points on the steps that have real impact (writes to CRM/ERP, customer communications, binding decisions). The AI prepares, the human confirms. For low-risk cases we operate with confidence rules and automatic fallbacks. No magic, always a trace.
Can I start with a small proof of concept before investing?
Absolutely yes: in fact, it's the approach we recommend for new use cases. We develop a proof of concept in 2-4 weeks, measurable on a limited scope, with success metrics defined up front. If it works, we scale. If it doesn't, we've avoided a bigger investment.
We start from a process, not from a model.
Tell us where you're losing time, quality or control today. We'll assess together if and where AI can really make a difference, with a measurable proof of concept if needed.