Chatbots, LLM features, RAG assistants and automated workflows, built on your own data and wired into the tools your team already uses.
Company assistant
Answers from your own documents
Works with the models and tools you already use
What we build
Most teams start with one workflow, prove the value in a few weeks, then expand. You own everything we build.
Website, WhatsApp, Slack and in-app assistants trained on your content, with your tone, clear hand-off to a human, and answers you can trace back to a source.
Includes: Web & WhatsApp widgets, Human hand-off, Multilingual, Conversation analytics
Summarisation, drafting, extraction, classification and copilots built into your SaaS, with prompt versioning, structured outputs, streaming UI and model routing to keep costs sane.
Includes: Model selection & routing, Structured outputs, Streaming UX, Usage & cost tracking
Retrieval-augmented generation over PDFs, Notion, Google Drive, Confluence, databases and help centres. Hybrid search, re-ranking and permission-aware retrieval so people only see what they are allowed to see.
Includes: Ingestion pipelines, Vector + keyword search, Source citations, Access controls
Tool-using agents that read, decide and act across your APIs: qualifying leads, triaging tickets, reconciling records, researching accounts. Every step is logged, and risky actions wait for human approval.
Includes: Tool & API calling, MCP servers, Approval steps, Full audit trail
End-to-end automations with n8n, Make, Zapier or custom code, with LLM steps where judgment is needed: reading emails, extracting invoice data, routing requests, generating reports.
Includes: n8n / Make / Zapier, Custom Node workers, Error handling & retries, Monitoring alerts
Opportunity mapping, quick prototypes, evaluation datasets, hallucination and safety checks, PII redaction and ongoing monitoring, so AI stays accurate after launch day.
Includes: Use-case workshop, Eval suites, PII redaction, Production monitoring
How RAG works
Retrieval-augmented generation lets an AI model read your policies, products and history at the moment it answers. Nothing needs retraining, and answers update when your documents do.
PDFs, Notion, Drive, help centre, databases
Split into passages, turn meaning into vectors
Vector + keyword store with access rules
Hybrid search and re-ranking per question
The LLM answers only from what was found
Every claim links back to its source
Answers from what it remembers of the internet. Sounds confident, can be out of date, and you cannot check where an answer came from.
Looks up your approved documents first, answers only from them, shows its sources and says “I don’t know” when the answer isn’t there.
Workflow automation
Plain automations break the moment someone phrases an email differently. We pair reliable tools like n8n, Make and Zapier with AI steps that understand the message, and keep a person in charge of the decisions that matter.
Use cases
Four places where AI usually pays for itself fastest. Most projects start with one of these.
The same questions arrive all day across email, chat and WhatsApp.
A RAG assistant answers from your help centre and policies, cites its source, and hands complex or angry conversations to a human with the full context.
Leads go cold while reps research, qualify and write follow-ups by hand.
An agent enriches each new lead, scores fit against your ICP, drafts a personalised reply and books the meeting, all logged in your CRM.
Invoices, forms and orders get retyped from PDFs and emails into spreadsheets.
Document extraction pulls fields into structured data, validates them against your rules and only flags the exceptions for review.
Answers live in Slack threads, old docs and one senior person’s head.
A private, permission-aware assistant over Drive, Notion and Confluence that new hires can ask anything, with links to the original document.
How we deliver
Step 1 · Week 1
We sit with the people doing the work, find the repetitive, high-volume steps, and agree on a measurable outcome: hours saved, response time, conversion or accuracy.
Step 2 · Weeks 2–3
A working prototype on your own documents and systems, not a slide deck. You test it, we measure it against an evaluation set built from real examples.
Step 3 · Weeks 3–6
Guardrails, permissions, fallbacks, logging, cost limits and integrations with your stack. Humans stay in the loop wherever the stakes are high.
Step 4 · Ongoing
We ship, watch real usage, review failure cases weekly and keep improving prompts, retrieval and models as better options appear.
Trust and safety
Accuracy, privacy and control are part of the build from day one, not something bolted on before launch.
Enterprise API terms that exclude training on your data, self-hosted or regional options when required, and secrets kept out of prompts.
Answers come from retrieved sources with citations. When the source is missing, the assistant says so instead of inventing one.
Refunds, emails to customers, record changes: any high-impact action can require a one-click approval before it runs.
Evaluation sets, tracing and cost dashboards from day one, so quality and spend are visible instead of assumed.
Nutshell Bytes designs, builds and runs production AI systems for startups and growing businesses: AI chatbots and assistants, custom LLM applications, retrieval-augmented generation (RAG) over company knowledge, AI agents that use tools, and end-to-end workflow automation connected to the software a team already uses.
Straight answers on RAG, models, data privacy, timelines and running costs. Ask us anything else on a call.
Still have questions?
Get in touch→RAG (retrieval-augmented generation) lets an AI model look up your own documents before it answers. Instead of relying on what a general model remembers, it searches your knowledge base, pulls the relevant passages and answers from them with citations. It is the most reliable way to get accurate, company-specific answers without retraining a model.
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