Technology · AI & Data
Language models from OpenAI, Azure OpenAI and AWS Bedrock, integrated safely.
Overview
Large language model (LLM) APIs from providers such as OpenAI, Azure OpenAI and AWS Bedrock let applications understand and generate text, extract structured data, call tools and work with images. They are consumed as cloud services, so there is no model to host or train.
Teams use LLM APIs to add capabilities that were impractical a few years ago: answering questions from documents, summarising, drafting and turning messy text into structured data. Doing it well is an engineering problem: grounding answers in your data, handling failures, testing quality and controlling cost.
What we build
Pipelines that chunk, embed and index your documents, retrieve the right passages and generate answers with citations.
Schema-constrained outputs that turn emails, PDFs and forms into validated records your systems can store.
Models that call your APIs through narrowly defined tools, with approval steps for consequential actions.
Test sets and automated scoring, so prompt or model changes are measured before they are released.
A thin internal layer that lets you switch between OpenAI, Azure OpenAI and Bedrock models without rewriting features.
Our approach
How we keep OpenAI & LLM APIs work tested, fast, secure and easy to release.
Every LLM feature gets a test set built from real examples and a quality bar agreed with the business before launch.
Prompt-injection defences, least-privilege tools, output validation and no secrets or unnecessary data in prompts.
Model choice by task, caching, context limits, token tracking per feature, and budgets with alerts.
Timeouts, retries with backoff, fallbacks to simpler behaviour and clear messages when the AI is unavailable.
Libraries, services and tools we commonly pair with OpenAI & LLM APIs.
Representative projects
A mid-size engineering consultancy
A permission-aware assistant that answers staff questions from procedures, project archives and templates, citing the source document every time.
Representative engagements. Client names and some details are withheld or illustrative.
FAQ
Can’t find what you’re looking for? Ask us directly. We’re happy to talk it through.
Both provide access to similar model families. Azure OpenAI runs inside Microsoft Azure, with regional deployment options and Azure’s identity and networking controls, which many Microsoft-centred organisations prefer. OpenAI’s own API often gets new models and features first. We help you weigh those trade-offs.
Yes, where they meet the quality bar. AWS Bedrock and Azure offer several open-weight models as managed services, and self-hosting is possible for specific needs, though it adds operational effort.
By not giving it data the user shouldn’t see in the first place: retrieval respects existing permissions, tools are scoped narrowly and outputs are validated before they are shown or acted on.
Rarely as a first step. Better prompts, retrieval and evaluation solve most problems more cheaply. Fine-tuning is worth considering only for narrow, high-volume tasks with plenty of good examples.
Keep exploring
Practical AI inside the software you already run: LLM features, assistants, Copilot and Power Platform automation, and the data work underneath.
Assistants, summarisation, document Q&A and content generation added to the products you already have.
Support and helpdesk assistants, and tool-using agents that act in your systems with human approval.
Copilot Studio agents, Power Automate flows and AI Builder document processing across Microsoft 365 and Dynamics 365.
Use-case discovery, feasibility and ROI assessment, data readiness, governance and a practical roadmap.
Tell us what you’re building and where you are today. A senior engineer will reply with questions, options and a sensible next step.
OpenAI & LLM APIs and other product names on this page are trademarks of their respective owners. Black Panther Digital Solutions is an independent company and is not affiliated with or endorsed by the vendors mentioned.