AI Development
Custom AI that does something your competitors can't buy off the shelf.
The transformative AI applications are the bespoke ones — custom LLM pipelines fine-tuned on your domain data, RAG systems that give models accurate knowledge of your business, and AI agents that automate complex multi-step workflows. We design and build production-grade AI systems that solve real problems, not impressive demos.
What you get
What's included in our
AI Development engagement
Production-Grade LLM Applications
Custom applications built on top of foundation models (GPT-4, Claude, Gemini, Llama) that are engineered for reliability — with prompt versioning, output validation, fallback handling, latency optimisation, and cost monitoring built in from the start, not as afterthoughts.
Retrieval-Augmented Generation (RAG) Pipelines
RAG systems that ground your LLM in accurate, up-to-date knowledge from your own documents, databases, and APIs — eliminating hallucination on domain-specific topics and giving you a foundation model that actually knows your business.
AI Agents and Autonomous Workflows
Multi-step AI agents that can research, plan, and execute complex tasks autonomously — browsing the web, querying APIs, reading documents, and taking actions in external systems. Designed with human-in-the-loop approval gates at critical decision points.
Our process
How we deliver AI Development
Use Case Definition and Technical Feasibility
We begin by precisely defining what the AI system must do, what quality thresholds define success, and what data is available for training and retrieval. We conduct a technical feasibility assessment before committing to an architecture, so you know what's achievable before you fund the build.
Rapid AI Prototype and Evaluation
We build a rapid prototype of the core AI capability — typically in 2–3 weeks — and evaluate it against a defined benchmark. This establishes a baseline performance level and validates that the chosen approach is the right one before full system engineering begins.
Production System Engineering
We build the production AI system with a focus on reliability, observability, and maintainability: API layer, caching strategy, prompt versioning, evaluation pipeline, cost monitoring, and integration with your existing systems. We treat AI as software engineering — with tests, documentation, and code reviews.
Deployment, Monitoring, and Model Management
Post-deployment, we configure LLM monitoring (response quality, latency, cost, error rates), set up evaluation pipelines to catch model drift, and establish a model management process for prompt updates and foundation model version transitions. AI systems require ongoing attention — we set up the infrastructure to provide it.
Stack
Technologies we use
Why Palsoro for AI Development
We Evaluate Before We Build
Before committing to a full AI build, we prototype and evaluate the core approach against measurable quality benchmarks. If the approach underperforms, we know early and can redirect — rather than discovering three months into a build that the base model can't reliably do what was required.
AI Engineering, Not Just AI Prompting
Effective AI development requires software engineering skills — vector database design, embedding pipeline optimisation, output validation, evaluation frameworks, and production monitoring. We bring full-stack AI engineering capability, not just prompt crafting.
Model-Agnostic Architecture
We design AI systems with model abstraction layers so you can switch foundation models as the landscape evolves — from GPT-4 to Claude to Llama — without rebuilding your application. The AI model is a component, not the foundation your entire system depends on.
RAG is generally the right starting point when you need the model to access specific factual information from your documents. Fine-tuning makes sense when you need to adapt the model's response style, tone, or domain-specific reasoning patterns that can't be reliably achieved through prompting alone. We evaluate both approaches during the prototype phase and recommend based on your specific requirements.
We define quality metrics at the outset — relevance, accuracy, factual grounding, format compliance, and task completion rate — and build an automated evaluation pipeline that scores model outputs against a benchmark dataset. This means quality is measured objectively, not just subjectively assessed by the person who built the system.
We implement data anonymisation, PII detection and redaction, access-controlled retrieval systems, and where required, on-premises or private cloud deployment so no data leaves your environment. We can build AI systems that never send your data to third-party model providers using self-hosted open-source models.
Model version transitions are part of our ongoing AI system management offering. We maintain a test suite for your AI application that can be run against new model versions before you cut over. We also design systems with abstraction layers so model swaps require minimal application-level changes.