Grounded answers
Retrieval-augmented generation against your real documents and tickets, with citations and confidence scoring so the bot says I do not know when it should.
ServiceLast updated
AI Chatbots & Assistants
Cut support response time without hiring more agents; AI assistants grounded in your real data, not hallucinated answers.
Service overview
Cut support response time without hiring more agents. We build customer-facing AI chatbots and internal knowledge assistants grounded in your real documents and tickets, with citations on every answer and clean handoff to humans when needed. The result is faster customers and a smaller support backlog.
Engagement
What this service includes.
Knowledge base ingestion and RAG setup
Channel deployment and escalation logic
Evaluation harness and tuning
What we offer
Why this service matters.
Smart escalation
Detects intent, sentiment, and uncertainty, and routes to the right human with context preserved. No more do-loops where the bot loses its place.
Continuous evaluation
Automated test suite of real user questions runs on every change, so we catch regressions before they hit production.
AI Chatbots integrations
Powerful AI integrations.
We pick the right model for your ai chatbots build, then blend providers behind a single internal interface.
OpenAI
GPT and embeddings. Broad ecosystem, strong structured-output and tool use, the safest default for general production.
Claude
Anthropic's frontier model. Our default for agents and long-context work where reasoning matters more than raw speed.
Gemini
Google's long-context multimodal family. Excellent for document and video pipelines, especially at scale.
Grok
xAI's model with live-web reasoning and a different blend of strengths. Useful for research-style and edge-case workloads.
DeepSeek
Open-weight models with strong cost-to-performance. We use it self-hosted when residency or unit economics demand it.
Perplexity
Citation-grounded search API for live-web augmented agents. Drops cleanly into RAG pipelines that need fresh sources.
Workflow ideas
Patterns we have built in ai chatbots.
Concrete workflows we have documented in this area. Each one ships behind your stack with the same engagement model as the service above.
FAQ
Questions about ai chatbots.
How do you stop the bot from hallucinating?
Retrieval-augmented generation against your real data, hard refusal when no relevant context is found, and a continuous evaluation harness running real questions on every change. Hallucinations are a system design problem, not a model problem.
Can it work on WhatsApp, Slack, our website, all of those?
Yes. We treat the underlying assistant as a service and deploy it to whatever channels matter. The brain is shared, the surfaces are interchangeable.
Where does its knowledge come from?
Your docs, tickets, knowledge base, product data, transcripts. We ingest, chunk, embed, and keep it in sync as your sources change. We do not rely on what the model already knows.
What does ongoing cost look like?
Two parts: model inference per conversation (usually pennies on modern models) and infrastructure for retrieval. We budget both upfront and tune as usage grows.
Get started
Ready to automate your operations?
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