Practical AI built into your operations
Practical AI integrations — automation pipelines, prediction models, and LLM-powered features built for production.
Start a projectMost AI pilots never make it to production because they're built as standalone demos disconnected from how the business actually works. We build AI integrations directly into your existing workflows and software, so the output is a feature your team actually uses — not a proof of concept that gets shelved.
That spans LLM integration for features like intelligent search and document Q&A, retrieval-augmented generation over your own data, document processing and classification pipelines, and prediction or anomaly-detection models trained on data you already collect. We work with both commercial APIs (OpenAI, Anthropic) and open-source models, choosing based on cost, latency, and data-handling requirements rather than defaulting to whichever is trending.
AI development at BitBySoft is treated as software engineering, not a separate discipline — the same standards around testing, monitoring, and maintainability apply, because a model in production that nobody can debug or improve isn't actually delivering value.
- LLM integration (OpenAI, Anthropic, open-source models)
- Retrieval-augmented generation and intelligent search
- Document processing, classification, and data extraction
- Prediction models and anomaly detection on your data
- 1Step 1
Identify the real workflow
We start from a specific, measurable operational problem — not "add AI somewhere" — and confirm there's enough usable data before committing to an approach.
- 2Step 2
Prototype against real data
We prototype quickly against your actual data to validate the approach works before investing in production integration, so we fail fast and cheap if it doesn't.
- 3Step 3
Integrate into the existing system
The AI feature is built directly into the software your team already uses — the same interface, the same login, the same workflow — rather than a separate tool to context-switch into.
- 4Step 4
Monitor and improve
Model outputs are monitored in production, since LLM and prediction behaviour can drift — we treat this as ongoing maintenance, not a one-time delivery.
- OpenAI
- Anthropic
- Open-source models
- RAG
- Node.js
- NestJS
- Python
- PostgreSQL
- Vector search
- Redis
- AWS
- Docker
- CI/CD
Questions about ai development
Ready to talk about your ai development project?
Tell us what you're building and we'll give you a clear, honest assessment. No sales calls, no commitment required.
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