AI automation and custom chatbot development
Most 'AI automation' pitches are a chatbot with a new name. Real automation replaces a multi-step manual process, not just a question — which means the engineering is in grounding it in your data, handling the steps that go wrong, and measuring whether it's actually faster than a person doing it by hand.
Concretely, what ships
Automating the process, not just the question
Where a chatbot answers a question, automation carries out the next several steps — pulling data from your systems, filling in a form, updating a record, triggering an approval. We map the full workflow before writing any AI, so the AI only replaces the steps that genuinely benefit from it.
Retrieval grounded in your own content
Your documentation, policies, product data and resolved tickets chunked, embedded and indexed, so the model answers from your material rather than its training data. Every answer carries the sources it used.
An explicit refusal path
When retrieval confidence is low, or the question touches something the assistant must not handle — refunds, account closures, legal or medical specifics — it hands off to a human with the context attached instead of improvising. Escalation is a designed feature.
An evaluation harness
A graded question set you own, run on every prompt or model change, so "we improved it" is a measurement rather than a feeling. This is what lets you switch models later without crossing your fingers.
Shadow mode before go-live
The assistant answers silently while your team grades the drafts. What it is reliably good at becomes the allowlist for automatic replies; everything else stays human. Automation is earned category by category.
Integrated into the tools you already use
Inside your existing helpdesk, CRM or product — not a second inbox your team has to remember to check, and no data migration.
Cost and latency control
Caching, prompt and context budgets, smaller models where they are sufficient, and per-conversation cost visibility so the bill does not surprise you in month three.
- 01
Find the boring, high-volume task
We look at where your volume actually is — a support queue, a manual data-entry process, an approval chain. The best first AI project is usually the dull repetitive one, not the impressive demo.
- 02
Prototype against real data
Built on your actual content and your actual historical questions within the first weeks, because a prototype on sample data tells you nothing about accuracy.
- 03
Evaluate, then shadow
Graded test set, then a live shadow period where a human reviews every draft. Nothing goes automatic until the numbers justify it.
- 04
Ship narrow, widen carefully
Go live on the categories that passed, monitor, and expand. Your team keeps the switch.
- You have a repetitive multi-step workflow — approvals, data entry, document processing — eating up staff time
- You have high-volume repetitive questions with knowable answers
- You need answers grounded in your own documents, not generic output
- A wrong answer or a wrong action has a real cost, so refusal and oversight matter
- You want it inside your current tools rather than a separate product
- OpenAI / Anthropic APIs
- Retrieval-augmented generation
- Embeddings + pgvector
- Workflow orchestration
- Python
- FastAPI
- Evaluation harness
- Helpdesk & CRM APIs
Vantage — Support AI Assistant
RAG-powered assistant resolving 41% of support tickets automatically, integrated with their existing helpdesk.
AI & Automation — common questions
If yours isn't here, ask us directly — we answer within one business day.
Is this just a chatbot, or can it actually automate our workflow?
Both, depending on what you need. A chatbot answers questions; automation goes further and carries out the next steps — updating a record, generating a document, routing an approval — after understanding the request. We scope this explicitly up front: which steps the AI proposes, which it's allowed to execute on its own, and which always go to a human. Most engagements start narrow — the AI proposes, a person executes — and widen once the evaluation numbers justify it.
What is RAG, and why does it matter for a business chatbot?
Retrieval-augmented generation means the system first finds the relevant passages in your own content, then asks the model to answer using only those passages. It matters because it is the difference between an assistant that knows your refund policy and one that invents a plausible-sounding refund policy. It also means you update the assistant by updating your documentation, not by retraining anything.
Will it make things up?
Any language model can. The engineering is in making that rare and visible: answers restricted to retrieved sources, citations attached so a human can check, a confidence threshold below which it escalates instead of answering, and hard blocks on topics you nominate. Then shadow mode measures how often it actually happens on your real questions before customers see any of it.
Do you use our data to train models?
No. Your content is indexed for retrieval, which is not training — nothing is baked into model weights, and removing a document removes it from the assistant's knowledge immediately. We configure provider settings so your data is excluded from their training, and we can discuss self-hosted or open-weight models if your requirements rule out third-party APIs entirely.
UI/UX design and design systems
Tell us what you're building
We'll come back within one business day with a clear plan and an honest estimate.