PELORA LABS®AI Built on the Model You Choose
Most agencies wire your product to one AI vendor and call it done. We build the feature and let you pick what powers it — OpenAI, Google Gemini, Meta Llama, Gemma or a private, self-hosted model — based on your cost, privacy and performance needs, not ours.
Why the model shouldn't be locked in
Every AI vendor wants you built on their model, because switching later is expensive — different APIs, different prompt behaviour, different pricing you didn't plan for. That lock-in serves the vendor, not you.
We build the integration layer so the model underneath is a configuration choice, not a rebuild. Start on OpenAI, move to a self-hosted Llama deployment later if the economics or the privacy requirements change — the feature you shipped keeps working.
AI Features, Engineered Into Your Product
Support, sales and product assistants trained on your own content, not a generic script.
AI search over your own data — documents, products, knowledge bases — with accurate, sourced answers.
Drafting, summarisation and content tooling built into your team's actual workflow.
AI steps embedded in existing processes — triage, tagging, routing, first-pass drafting.
AI-assisted analysis that turns raw data into something a non-technical team can act on.
Self-hosted deployments for organisations where data can't leave their own environment.
Pick the Model That Fits the Job
Different models trade off cost, privacy, speed and capability differently. We help you choose — and build so the choice can change later without a rebuild.
The broadest ecosystem and strong general-purpose reasoning — the default choice when you want proven capability and fast time to launch.
Native multimodal input and tight integration with Google Cloud — a strong fit if your stack already leans on Google's infrastructure.
Open-weight and self-hostable, so inference can run on your own infrastructure — no per-token bill and no data leaving your environment.
A lighter open-weight model built for smaller, faster or edge deployments where a full-scale model is more than the task needs.
Also available on request: Anthropic Claude, Mistral and other open-weight models — if it has an API or weights we can self-host, we can build on it.
What Model Choice Actually Buys You
Control
- No single vendor's price rise becomes your emergency
- Sensitive data can stay on infrastructure you control
- Swap models as capability and pricing shift, not on their schedule
- Match the model to the task instead of over- or under-paying for it
Proof, not theory
- We've shipped this — see the Sovata AI case study
- Private, on-premise AI built for enterprises that can't send data out
- The same architectural thinking applies at any scale
- Model-agnostic from day one, not bolted on after a vendor lock-in problem
From Idea to Running Feature
Discovery
We map the actual problem the AI feature needs to solve — and whether AI is even the right tool for it.
Model selection
We weigh cost, privacy, latency and capability against your requirements and recommend a model — with reasons.
Build & integrate
The feature is built into your product with a clean integration layer, so the model is a configuration, not a hard dependency.
Monitor & tune
We track cost, accuracy and performance in production and adjust — including switching models if the numbers say to.
Let's Talk About What AI Should Actually Do for You
Book a free consult. We'll tell you honestly whether AI is the right tool for your problem — and if it is, which model fits.
Book a Free AI Consult→