AI Integration
Add AI capability to the software you already have.
You don't need to rebuild your product to benefit from AI. We embed OpenAI, Anthropic, and Google AI APIs into your existing applications — adding intelligent search, automated drafting, classification, summarisation, and copilot features that enhance what your software already does without a ground-up rebuild.
What you get
What's included in our
AI Integration engagement
AI-Powered Features Inside Your Existing Product
We add AI capabilities — intelligent search, content generation, classification, summarisation, anomaly detection — as features within your current software rather than replacing it. Your users get AI superpowers through the interface they already know.
Copilot and Assistant Interfaces
Embedded AI assistants that understand the context of what users are doing in your application and offer relevant suggestions, automated drafts, or guided next steps — reducing time-to-value for complex tasks and reducing the expertise required for routine operations.
Reliable API Integration with Cost Controls
AI API integrations that are production-reliable: with caching, rate limit handling, fallback logic, token usage monitoring, per-user budget controls, and latency optimisation. Your AI features will work at 3am under peak load, not just in a demo environment.
Our process
How we deliver AI Integration
High-Value AI Feature Identification
We review your existing product and user workflow to identify where AI capabilities deliver the most value — not where they're most technically interesting. We prioritise features that save significant time, reduce errors, or enable things users currently can't do at all.
UX Design for AI Features
AI features have unique UX requirements — handling latency, displaying uncertainty, managing errors gracefully, and giving users the right level of control over AI suggestions. We design AI feature UX specifically, not by treating AI outputs like any other data.
API Integration and Backend Engineering
We implement the API integrations, build the prompt engineering layer, design context assembly pipelines, configure caching and rate limiting, and connect the AI capability to your existing data sources. Every integration is built with test coverage and monitoring.
Launch, Measure, and Iterate
We instrument AI features with adoption metrics, quality feedback mechanisms (thumbs up/down, explicit corrections), and cost tracking. The first 30 days post-launch provide the data to improve prompts, adjust feature design, and identify which AI capabilities users actually rely on.
Stack
Technologies we use
Why Palsoro for AI Integration
We Know Which AI Features Actually Get Used
We've shipped AI integrations that users love and ones they ignore. The difference is almost always in the UX design and the context precision — not the underlying model. Our experience knowing where AI integrations succeed and fail in production informs every feature we design.
Provider-Agnostic, Best-Model-for-Task
We select the model and provider based on your specific task requirements — GPT-4o for complex reasoning, Claude for long document analysis, Gemini for multimodal tasks, local models for privacy constraints. We're not committed to one provider's commercial interest.
Cost Management Is Part of the Engineering Brief
AI API costs can escalate rapidly at scale. We build cost controls into every integration: semantic caching to reduce redundant API calls, token-efficient prompt design, tiered quality levels for different use cases, and usage dashboards so you're never surprised by an API bill.
Simple AI features — a summarisation button, a classification tag, an assisted text field — can be integrated in 2–4 weeks. More complex features like a full copilot assistant with context awareness typically take 6–12 weeks. We scope each feature individually and provide a fixed-time estimate before work begins.
Yes. AI can be embedded as background automation rather than user-facing features — auto-categorising submitted forms, scoring leads, or generating draft responses that a human reviews before sending. We design for both transparent AI features and invisible AI automation depending on your user expectations.
We build graceful degradation into every AI feature — if the AI API is unavailable, times out, or returns a low-confidence output, the system falls back to manual operation or cached results rather than showing an error to the user. We also implement output validation to catch and handle clearly incorrect AI responses before they reach users.
OpenAI, Anthropic, and Google all offer enterprise API agreements with data processing terms and options to opt out of training data use. We implement data minimisation by default — sending only the minimum context needed for each task. For high-sensitivity requirements, we use on-premise model deployments that eliminate third-party data exposure entirely.