AI Integration
We add practical AI to your product or website — assistants that actually know your business, document processing and workflow automation — using OpenAI, Gemini and open models, with the guardrails done properly.
AI integration at DOCXA is engineering, not prompt-stuffing. We identify where AI genuinely earns its keep in your operation — support answers, document handling, content drafts, data extraction — then build it into your existing site or software with retrieval over your real data, evaluation before launch and cost controls after. The output is a feature your team trusts, measured against the boring baseline it replaced.
What's included
- Discovery: which workflows AI genuinely improves, with a cost/benefit baseline
- AI assistant grounded in your own documents and data (retrieval-augmented, not generic)
- Document extraction and processing pipelines with human-in-the-loop review
- Integration into your existing site or software — no separate silo
- Guardrails: answer grounding, refusal rules, escalation to humans
- Cost monitoring and rate limits so the bill never surprises you
Built for ai integration buyers like you
Teams buried in repetitive support
An assistant grounded in your docs resolves the routine 70% so your people handle the real 30%.
Businesses doing manual document work
Invoices, applications, forms — extraction and routing that used to take hours per day.
Products that need smart search
Customers finding things by meaning instead of exact keywords converts measurably better.
Owners told to 'add AI' with no roadmap
We turn the buzzword into one shipped feature with a measurable baseline — then decide what's next.
Problems this solves
A chatbot widget that answers everything except questions about your actual business
Staff re-typing data from PDFs and emails into systems by hand
AI experiments that demo well but can't be trusted with real customers
No visibility into what AI features actually cost per month
Hallucinated answers heading straight to your customers
Valuable knowledge trapped in documents nobody has time to search
What you get
- Discovery: which workflows AI genuinely improves, with a cost/benefit baseline
- AI assistant grounded in your own documents and data (retrieval-augmented, not generic)
- Document extraction and processing pipelines with human-in-the-loop review
- Integration into your existing site or software — no separate silo
- Guardrails: answer grounding, refusal rules, escalation to humans
- Cost monitoring and rate limits so the bill never surprises you
- Evaluation set created from your real cases, tested before launch
- Team handover: how to update the knowledge base and read the metrics
Technologies we use
Models
Retrieval & data
Application layer
Control
How the work actually runs
- 01
Baseline & pick the target
One workflow, measured: current time, cost and error rate define what success means.
- 02
Prototype on your data
A working prototype built on your real documents within days — so decisions are based on evidence.
- 03
Engineer the guardrails
Grounding, refusals, escalation paths and evaluation before anything meets a customer.
- 04
Ship inside your product
Integrated into your site or software with monitoring and cost controls live from day one.
- 05
Measure & expand
Against the baseline: if it wins, we expand to the next workflow; if not, we say so.
Timeline
A focused AI feature typically ships in 2–6 weeks. Multi-step document pipelines and deep integrations run 6–10 weeks depending on source complexity.
How pricing works
Two honest lines: our fixed build quote, and the model-usage costs shown at actuals — most business features run on surprisingly small monthly inference bills once caching and routing are done right. We'll kill a feature in discovery if the baseline shows AI isn't the right tool for it.
Industries we serve
Frequently asked questions
How do you stop the AI from making things up?
Grounding and humility by design: the assistant answers from your retrieved documents, cites them, and is engineered to say 'I don't know, let me get a human' rather than improvise. An evaluation set built from your real questions is tested before launch — and after.
What does an AI feature cost per month to run?
Usually less than clients expect once caching, right-sizing and routing are handled: the inference bill for most support assistants is a modest monthly line item. We build cost dashboards in from day one so you always see the actual number.
Will our data leak into public models?
We use provider APIs with commercial no-training terms, send only what the feature needs, and keep your data in your own retrieval store. For stricter requirements we deploy open-weight models on infrastructure you control.
Which AI model should we use?
Whoever serves your case best this quarter — GPT, Gemini or Claude all lead on different tasks, and prices shift constantly. The integration layer makes models swappable, so you're never married to one vendor's roadmap.
We already tried a chatbot and it was embarrassing. Different how?
Generic chatbots answer from nothing; ours answer from your documents with citations, refuse gracefully outside their lane, and hand off to a human cleanly. The difference isn't the model — it's the grounding, evaluation and escalation work around it.
Enquire about AI Integration
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