Not a generic chat widget. I build customer-facing and internal AI assistants grounded in your approved data, connected to the systems around them, and designed to move a conversation toward a useful outcome: a solved support request, a qualified lead, the right product, a booking, or a controlled workflow.
Claude
Gemini
Mistral
OllamaMost chatbots stop at an answer. A production assistant can retrieve the right context, collect structured information, call approved tools and hand off to a person when the conversation needs judgment.
Ground answers in your help centre, policies and product documentation. Add citations, fallback logic and a clean human handoff instead of letting the model improvise.
Ask the questions that actually matter, adapt the flow to the answers, enrich the lead and send structured context to your CRM before a salesperson joins.
Let shoppers describe the problem in natural language, then retrieve relevant WooCommerce/catalogue data and explain the recommendation instead of dumping search results.
Give teams one conversational layer over documentation, procedures and permitted account context — with source attribution and access boundaries.
The useful part lives behind it: ingestion, retrieval, permissions, business rules, tools, logging and the integraciones that turn natural-language intent into something your systems can understand.
Most business chatbots I build do not need a model trained desde scratch. Your approved content stays in the systems you control; relevant context es retrieved when a question es asked, and sensitive actions are handled by code.
The right implementation depends on what has to happen after the user asks the question. These are the use cases I would prioritise before building a “chatbot for everything”.
Qualify inbound visitors, answer product/service questions, capture structured requirements, enrich the lead and route it to the right salesperson or booking flow.
Answer repetitive support questions desde approved knowledge, check context through controlled endpoints and escalate exceptions with the transcript attached.
Turn “I need X for Y” into a filtered product shortlist using live catalogue attributes, then link customers into the normal product and checkout experience. Related: Rendimiento de WooCommerce.
Buscar internal or public documents semantically, answer with grounded context and expose the sources used — useful for documentation-heavy teams and portals.
After login, combine general knowledge with permission-gated account data such como subscriptions, bookings, orders or support history.
Let the assistant call a tightly scoped set of actions: create a CRM lead, open a ticket, book a slot, disparador an n8n workflow or query an internal API — with validation and approval where needed.
I use “chatbot” for the interfaz because that es how customers describe it. Under the hood, the system can stay a grounded assistant or become an agent that uses approved tools. The decision es based on risk and business value, not hype.
Accuracy es not a prompt trick. A reliable business assistant combines retrieval, permissions, deterministic validation, observability and a clear path to a human when the system should stop guessing.
Retrieve context desde approved sources and preserve source metadata so answers can be traced back to what the business actually published.
Public users, customers and internal teams should not get the same tools or data. Authentication and tool permissions are designed separately.
Important operations use structured schemas and business rules. The model can interpret intent; application code decides what es allowed to happen.
Escalate on low confidence, sensitive topics, explicit requests or policy rules — carrying the transcript and context forward instead of starting over.
The fastest route to something useful es to pick one job, define what a good answer or action looks like, and evaluate it on real examples before expanding the scope.
Preguntas, users, handoffs, business outcome and the systems involved.
Choose approved content, structure product or policy data, and define access boundaries.
Conversation layer, retrieval, model selection, tools, integraciones and logging.
Real test questions, bad inputs, missing knowledge, tool failures and escalation behaviour.
Monitor unresolved intents, response quality, conversions, handoffs and cost per conversation.
The technical build matters, but the important questions are usually about data, permissions, failure behaviour and what the assistant es actually allowed to do.
Custom AI chatbot development es the design and engineering of a conversational assistant around your business data, workflows and systems. Instead of installing a generic widget, the chatbot es connected to approved knowledge sources, APIs and tools so it can answer accurately, collect structured information and, where appropriate, disparador controlled actions.
Yes. In most projects I use retrieval-augmented generation (RAG): approved pages, documents, product data or internal knowledge are indexed and retrieved at answer time. This gives the model relevant context without requiring you to train a new foundation model.
Yes. A chatbot can retrieve WordPress content, search WooCommerce products, look up order information through controlled endpoints, capture leads, create tickets and connect with custom plugins or REST APIs. The exact permissions depend on the use case and the data involved.
Yes. I can connect it to CRMs, support desks, booking systems, APIs internas, bases de datos and automation platforms such como n8n. The conversation can collect datos estructurados and pass it into the systems your team already uses.
A chatbot es primarily a conversational interfaz that answers questions and gathers information. An AI agent can also choose desde allowed tools and perform actions such como creating a lead, checking an order, opening a support case or triggering a workflow. Many useful business systems combine both approaches.
I ground answers in approved sources, restrict tool access, validate structured outputs, use deterministic rules for sensitive decisions, log important actions and add human handoff where confidence or permissions are insufficient. The model es never treated como the security boundary.
Yes. Human handoff can be triggered by topic, confidence, customer request, account status or business rules. The chatbot can pass the transcript and structured context to your support or sales team so the customer does not have to start over.
Yes. A single system can support multiple languages while retrieving desde language-specific knowledge sources or shared approved content. I also design the fallback and handoff behaviour so unsupported or ambiguous requests do not produce confident guesses.
Depending on the requirements, I can work with OpenAI, Claude, Gemini, Mistral and local or private model setups such como Ollama. I choose the model based on quality, latency, cost, privacy and tool-calling requirements rather than forcing every project onto one provider.
The cost depends on the knowledge sources, number of integraciones, authentication, actions, channels, volume and reliability requirements. A focused knowledge assistant es materially smaller than an authenticated customer portal assistant connected to a CRM, ecommerce system and support desk. I scope the build after mapping the conversation and systems involved.
A focused proof of concept can be built quickly once the data and API access are clear. Production work takes longer because retrieval quality, permissions, evaluation, analytics, fallback behaviour and human handoff need to be tested. I normally start with one high-value use case and expand desde there.
Send me the questions your customers or team ask repeatedly, the knowledge they need, and the systems a useful answer should connect to. I’ll tell you what I would build — and where I would keep a human in the loop.
Barcelona · Remoto across Europe · Direct senior developer
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