AI Integration for Business Workflows
Tuxxin builds large-language-model APIs into the systems your business already runs: customer support, sales follow-up, content drafting, document parsing, translation and voice phone agents.
AI features run on the providers' own APIs (Anthropic Claude, Google Gemini and OpenAI) through API accounts in your company's name, so usage is billed at the provider's rates. Tuxxin builds voice phone agents on Twilio, chatbots with retrieval-augmented generation, document classification, support-ticket triage and content drafting pipelines.
AI Services We Deliver
Customer-Support Chatbots
Grounded in your knowledge base (docs, support tickets, FAQs) with retrieval to reduce hallucinations. A widget for your site, connected to your help desk for handoff to a person.
Voice Phone Agents
An AI receptionist on a Twilio number that books appointments, answers FAQs, qualifies leads and routes calls to the right person, with bilingual support.
Content Generation
AI-assisted blog drafts, product descriptions and meta descriptions, in pipelines that match your brand voice and keep an editor in the loop.
Multilingual Translation
Site copy translated across dozens of languages. Tuxxin built this pipeline for webshot.site (27 locales with full hreflang) and can deploy it for your site.
Document Parsing
Structured data from PDFs, scanned invoices, contracts and forms. AI handles OCR and classification; validation, retry logic and a human-review queue catch the rest.
Lead Scoring and Triage
AI scores the intent of contact-form leads and sales emails, drafts a response and routes each one to the right person.
Our AI Stack
Models
Anthropic Claude for reasoning and tool use, Google Gemini Flash for high-volume work and OpenAI models where a project calls for them. Each task is benchmarked before a model is chosen.
Frameworks
Direct API calls with light orchestration through the Anthropic Agent SDK or an equivalent, with tool use, structured output and prompt caching built in.
Voice and Telephony
Twilio for inbound and outbound calls, with speech-to-text and text-to-speech from the provider or a self-hosted Whisper and Coqui pipeline.
Retrieval (RAG)
pgvector or sqlite-vss for embeddings, with Voyage AI or OpenAI embedding models. Documents are indexed at deploy time, so each query searches a ready index.
Cost Controls
Per-tenant token budgets, prompt caching, model fallback chains and cost alerts, so you can see what each customer interaction costs in real time.
Privacy
PII redaction at the prompt boundary, opt-in data retention with the LLM provider and audit logs of every model call. Local models such as Llama and Mistral can run on your own servers where compliance requires it.
Our Delivery Process
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Identify the Use Case
A working session to find the steps in your workflows that suit an LLM: high volume, language-heavy and currently manual.
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Prompt and Benchmark
Two or three prompt strategies are prototyped and benchmarked for accuracy and cost on real samples. You see the figures before committing.
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Integrate
The winning prompt is wired into your application through the API, with retry logic, cost caps and a fallback when the model is unavailable.
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Measure and Iterate
Every call is instrumented for latency, token cost and accuracy on review samples, and tuned monthly like any other production service.
AI Integration Is Right for You If
- You handle a high volume of customer messages, support tickets or inbound calls and want to triage them faster.
- You produce product descriptions, blog posts or meta tags at a volume your staff cannot write by hand.
- You translate your site or app into several languages.
- You receive invoices, forms or contracts and want structured data out of them without hand-keying.
- You want ChatGPT-style features inside your own application.
Frequently Asked Questions
Discuss an AI Use Case
Describe the workflow you want to improve. Some jobs need an LLM, and some need a 50-line Python script.
Book a Scoping Call