channel c · ai automation
AI Automation and Chatbots
Chatbots with real context and workflows that remove manual work. With spend limits, conversation logs, and a human who can step in.
Scope a process →All services →
Based in Mexico City. Available remotely.
context
Why this matters
AI automation isn't about adding a chatbot for the sake of it. It's about identifying which processes in your business consume the most human time, and engineering solutions that handle them reliably, securely and at scale.
I build chatbots that hold context and memory, automated workflows that process documents, and API integrations that connect your existing systems to an LLM — always with security as a design principle rather than a later add-on.
Before any code is written I put two things in writing: which information never reaches the model, and the exact point at which a conversation is handed to a person. An LLM gets things wrong, and a system with no plan for that case is not finished.
Cost is designed, not discovered on the invoice: caching for what repeats, a model chosen to match the difficulty of the task, and a dashboard where you can read what each conversation actually cost.
scope
What I automate
Intelligent chatbots and AI-powered process automation
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- Context-aware chatbots that understand your business
- Secure integration with your existing systems (CRM, ERP, APIs)
- Measurable operational ROI: less manual work, more efficiency
What is different afterwards
- Repetitive customer questions get answered without someone on your team typing them again.
- Processes that today live in one person’s head end up documented and executed by a workflow.
- Every answer is logged: you can audit what the system replied and which data it used.
- A bounded cost per conversation, using caching and a model chosen to match the difficulty of the task.
How I work
How the work runs
Four steps, in this order. The limits are agreed before anything is built.
- 01
Process inventory
We list which tasks are candidates for automation and which are not. The ones that depend on human judgement stay with humans, and that is written down.
- 02
Conversation and data design
Flows, prompts, knowledge sources, and above all which information must never reach the model. The boundaries are set before anything is built.
- 03
Implementation and integration
Building the chatbot or workflow, connecting it to your systems (CRM, database, APIs), and adding authentication, input sanitization, and rate limits.
- 04
Measurement and tuning
Usage dashboard, reading real conversations, and tuning prompts, caching, and human-escalation paths against production data.
fit
Who this is for — and who it is not
A good fit
- Support or sales teams answering the same set of questions every day.
- Operations with repetitive, well-defined processes that are still done by hand.
- Businesses with useful internal documentation that nobody finds in time.
Not a good fit
- Not for anyone who wants the system to give the final ruling on a legal, medical, or financial matter. An LLM gets things wrong, and there the cost lands on your client: a person signs that answer, or I do not build the flow.
- Not for processes that are not documented yet. Automating a confused process only makes it confused faster and harder to correct.
What's included
What you get
- Automatable process analysis and feasibility study
- Conversational flow and dynamic prompt design
- Chatbot development with LLM integration (GPT/Gemini)
- Integration with APIs and internal systems
- Monitoring dashboard and usage metrics
- Technical documentation and training
Stack
Models and integrations
- Models
- GPT · Gemini · Claude
- Runtime and orchestration
- Python · Node.js · TypeScript · LangChain · Next.js
- Data and integrations
- REST APIs · GraphQL · WebSocket · Firestore · PostgreSQL · HubSpot · Salesforce
FAQ
Frequently asked questions
The questions this service gets before the first call.
Can the chatbot connect with my CRM or database?
Yes. I design integrations with HubSpot, Salesforce, Firestore, PostgreSQL, and REST/GraphQL APIs. The chatbot can query and write data to your systems securely.
How secure is the implementation?
Security is a design principle, not an add-on. I implement JWT authentication, input sanitization, rate limiting, and sensitive information is never sent directly to the LLM without preprocessing.
How much does it cost to maintain an AI chatbot?
Operational costs depend on conversation volume and the AI model used. I design architectures that optimize token usage with caching and precomputed responses to keep costs low without sacrificing quality.
keep reading
Related services
- ch bModern Web Apps with Next.js & FirebaseFast, indexable, modern web application development
- ch dResponsive DashboardsInteractive, data-driven dashboards for your platform
Not sure which one you need? compare all four services, or describe the process on the contact page.
the proof · file pending
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three channels · parallel
How you reach me
- ch aThe formFive fields, and the URL is the one that helps most. If the site comes with it, my first reply already has a reading in it.
- ch bWhatsAppFor a thirty-second question. It opens with the subject already written, so nothing has to be explained twice.
- ch cBook a callThirty minutes, calendar open. Pick the slot that works and turn up with nothing prepared.
or write to carlos@carlosanayaweb.com · +52 55 4416 7974
Scope a process
Tell me who runs the process today, how often it runs, and what happens when it goes wrong. I reply in under 24 hours with whether it is worth automating and what the first step would be.
Book a call →carlos@carlosanayaweb.com
If the process shouldn't be automated, I'll say so on the first call.