Technology & Automation
AI Software Development
Practical AI features and integrations built into real products — not a chatbot bolted onto a homepage. We build AI that works in production, not just in demos.
AI is only useful when it's built into something real
Most businesses experimenting with AI are stuck at the ChatGPT-tab-open stage — useful for one-off tasks but not embedded in any actual workflow or product. The real leverage is integrating AI directly into the tools and processes your team already uses: automating support, processing documents, building internal knowledge tools, running content pipelines. We build practical AI integrations that reach production — not proof-of-concept demos that never ship.
What we typically build
Real scenarios — not abstract capabilities.
Customer support AI assistant
A RAG-based support assistant trained on your documentation and product knowledge — answering customer queries accurately, with citations, and escalating to humans when it genuinely can't help.
Internal knowledge base
Your team's scattered documents, SOPs, Notion pages, and PDFs become a queryable knowledge base — answer questions in seconds instead of digging through folders.
AI content & research pipeline
Multi-step AI agents that research a topic, extract key points, draft structured content, and route it through a review workflow — cutting production time without cutting quality control.
Document processing & data extraction
Invoices, contracts, applications, or reports processed automatically — relevant fields extracted, validated, and pushed to your systems without manual data entry.
What's included
AI readiness audit
An honest assessment of where AI will genuinely help your business versus where it adds complexity without value — before any build work begins.
AI API integrations
Claude, OpenAI, and other model APIs connected into your existing software with clean, maintainable integration layers.
RAG & knowledge base systems
Document ingestion, vector search, and retrieval pipelines that return accurate, cited answers from your own content.
AI agents & automation pipelines
Multi-step AI workflows that reason through a task, use tools, and produce structured outputs — built to run reliably in production.
Proof of concept builds
A working prototype that validates the use case and the technical approach before committing to a full production build.
AI-powered internal tools
Purpose-built tools for your team — dashboards, assistants, and processing interfaces with AI at the core.
Works with your stack
We integrate with the tools your team already uses — no ripping and replacing.
Anthropic Claude API
LLM
OpenAI API
LLM
Pinecone
Vector DB
pgvector / Supabase
Vector DB
LangChain
Orchestration
LlamaIndex
Orchestration
Python / FastAPI
Backend
Next.js
Frontend
Is this the right fit?
Right for you if…
- ✓SaaS founders who want to add AI features but need the technical implementation handled
- ✓CTOs evaluating AI approaches and wanting a pragmatic, stack-specific recommendation
- ✓Operations teams with large volumes of repetitive document processing or data entry
- ✓Businesses with proprietary knowledge they want to make searchable and queryable
- ✓Product teams who've been exploring AI for months but haven't shipped anything to production yet
Probably not a fit if…
- ✗Businesses looking for an off-the-shelf AI chatbot that can be set up without development work
- ✗Teams that need custom model training or fine-tuning — we work with existing APIs, not model training
- ✗Projects with no clearly defined use case or problem to solve yet
How we deliver AI Software Development
The same order, every engagement — because the sequence matters.
- 01
AI readiness audit
Define the specific use case, the data inputs available, the expected outputs, and the success criteria — before any code is written.
- 02
Proof of concept
Build a working prototype to validate the approach and surface edge cases. Whether to proceed should be based on evidence, not optimism.
- 03
Production build
Secure, maintainable code integrated into your actual stack — with proper error handling, fallback behaviour, rate limiting, and logging.
- 04
Testing & evaluation
Accuracy benchmarking, edge case testing, and safety checks. For RAG systems, that means measuring retrieval quality and hallucination rate — not just whether it 'seems to work.'
- 05
Deploy & iterate
Production deployment plus optional iteration retainer for prompt tuning, model version updates, and expanding the system as your needs grow.
Tools we use to deliver this
Best-in-class tools, applied by practitioners who know what the data actually means.
Anthropic Claude API
Primary LLM for most integrations — strong reasoning, instruction following, and long-context handling.
OpenAI API
GPT-4 and embedding models for specific use cases where performance trade-offs make it the better fit.
Pinecone / pgvector
Vector databases for semantic search and retrieval in RAG systems — Pinecone for managed scale, pgvector for Postgres-native simplicity.
LangChain / LlamaIndex
Orchestration frameworks for complex agent workflows and document ingestion pipelines.
Python / FastAPI
Backend layer for AI services — clean, well-typed APIs that your existing systems can call.
Next.js
Frontend for AI-powered tools and interfaces when a full user-facing layer is needed.
Why 365Digital
We build AI integrations that actually ship to production
- →We've built RAG systems, document processing pipelines, and AI-integrated applications — not demos, but production deployments used by real users.
- →We use Claude and OpenAI APIs ourselves for internal tooling — so our recommendations come from real implementation experience, not vendor documentation.
- →Every project starts with an honest readiness audit. If the use case isn't well-defined or the data isn't ready, we'll tell you before you spend budget on a build.
- →We build with security and reliability in mind — API key management, rate limiting, fallback behaviour, and logging are part of the spec, not an afterthought.
- →You own everything we build. Full codebase handover, no proprietary frameworks, no lock-in to our infrastructure.
Results from this kind of work
Sample layout — real results pending
[SCREENSHOT: AI Software Development case study — before/after metric]
[SCREENSHOT: AI Software Development dashboard or workflow]
How we price this service
Every AI project starts with a readiness audit to define the use case and validate the approach. From there, the build is scoped and priced on a fixed-fee basis — with a clear deliverable and timeline.
Starting from [USD $X,XXX] per project
Common questions
We're not sure if AI is right for our business yet — where do we start?
+
An AI readiness audit is the right starting point. We look at your current workflows, the data you have available, and where AI would genuinely reduce time or cost — versus where it adds complexity without enough value. You leave with a prioritised list of real opportunities, not a generic AI strategy document.
How do you handle data privacy when using AI APIs?
+
We don't send sensitive data to AI APIs without a clear legal basis and your explicit sign-off. For use cases involving personal or confidential data, we evaluate self-hosted or private deployment options (like Azure OpenAI or Anthropic's enterprise offerings) and advise accordingly.
What's the difference between using a pre-built AI tool and custom AI development?
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Pre-built tools are fast to set up but constrained — you get what the vendor built for the average use case. Custom development means the AI integration fits your specific workflow, your data, and your quality bar. The trade-off is time and budget; the payoff is something that actually works the way your business works.
How accurate are RAG systems — can we trust the answers they give?
+
Accuracy depends heavily on the quality of your source content and how the retrieval system is tuned. In our builds, we measure retrieval precision and answer quality systematically — not just subjectively. For critical use cases, we design the system to cite its sources so users can verify any answer before acting on it.
Do you work with models other than Claude and OpenAI?
+
Primarily Claude and OpenAI, as these cover the vast majority of use cases well. If your use case benefits from a specific model — Gemini, Mistral, or a fine-tuned open-source model — we can evaluate and integrate it. We're model-agnostic in principle; pragmatic in practice.
What happens after the project is built — who maintains it?
+
We offer an optional iteration retainer post-launch — covering prompt updates, model version changes, expanding the system to new use cases, and monitoring. For simpler integrations, the codebase is clean enough that your own team can maintain it with no dependency on us.
Related services
Ready to see where you actually stand?
A free audit covers your current SEO health and where AI automation could realistically save you time — no generic sales pitch.