01
Turn a vague "we should use AI" idea into a scoped, secure, production-ready application with a defined data flow, permission model and approval path.
Preparing experience
Claude, OpenAI, Python and FastAPI
AI Projects
We design and build production AI applications on the Claude (Anthropic) and OpenAI platforms—AI agents, LLM-powered chatbots, workflow automation tools, AI-integrated SaaS features and retrieval-augmented (RAG) internal tools—served through Python FastAPI backends with evaluation, guardrails and human review built in from the first release, not added after something goes wrong in production.
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Capabilities included
What this type of engagement typically covers.
- Claude & OpenAI APIs
- Python FastAPI backends
- RAG & vector search
- Agentic workflows
Why WLC
Built for buyers who need clarity, speed and accountability.
Three principles that shape how we scope, build and deliver every engagement.
02
Ground LLM outputs in your own documents, tickets, contracts and business data through retrieval-augmented generation, instead of relying on model memory or generic training data alone.
03
Automate multi-step operational work with AI agents that call your APIs and trigger real approvals—not just draft text for a human to copy elsewhere.
04
Embed AI directly into existing products—search, summarisation, classification and personalisation—without standing up a separate bolt-on tool.
05
Ship with evaluation suites, guardrails and observability from day one, so model behaviour is measured and monitored rather than assumed to work.
06
Keep humans in the loop wherever the cost of an AI mistake is high, with clear escalation paths designed into the workflow rather than left as an afterthought.
Delivery model
A clean path from idea to launch.
Use-case mapping
Architecture & guardrails
Prototype
FastAPI build
Evaluation & launch
Questions buyers ask
Common questions before starting a project.
Both. We pick the model (or combine several) based on the task, latency and cost profile, and build a thin provider-abstraction layer so you can switch or add models later without rebuilding the application around a single vendor.
Internal copilots and customer-facing LLM chatbots, RAG-based knowledge tools over documents and support tickets, agentic workflow automation that calls your APIs, and AI features embedded directly into existing SaaS products—search, summarisation, classification and personalisation.
Retrieval grounding against your own data, output evaluation suites, confidence thresholds and human review checkpoints for high-stakes actions, combined with logging so every model decision can be audited after the fact.
Python and FastAPI power the API layer, with a vector store for retrieval, background job queues for longer-running agent workflows, and standard observability so you can see latency, cost and failure rates per request.
Yes—most engagements integrate into an existing CRM, help desk, ERP or internal tool rather than replacing it outright, connecting through APIs, webhooks or a lightweight service layer we build alongside your system.
We scope exactly what data is sent to which provider, apply redaction where required, and can design for private or VPC-hosted model deployments where compliance or contractual terms demand it.
Related pages
Continue exploring WLC capabilities.
Ready to move
Tell us what you are building — we will map a practical route.
Share the outcome, timeline and constraints you are working with. We will respond with useful next questions and a clear way to start.