AI & Machine Learning

AI Engineers Who Ship to Production

Building AI features is easy. Building AI features that are reliable, observable, and cost-effective in production is hard. Pears AI engineers specialise in LLM integration, RAG pipelines, fine-tuning, and MLOps — turning prototypes into products your customers can depend on.

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5 days

Average onboarding

50–70%

Cost saving

100%

Pre-vetted

Why Pears

What You Get

LLM & RAG Specialists

OpenAI, Anthropic, Mistral, and open-source LLMs. We design retrieval pipelines that answer accurately — without hallucinating.

Production MLOps

Model serving, versioning, A/B testing, and monitoring. We keep your AI reliable as data and models evolve.

Cost-Aware Architecture

Token budgeting, prompt caching, and model selection keep your AI bills predictable.

Fast Iteration

AI moves fast. Our engineers ship experiments weekly — not quarterly.

What's Included

RAG (Retrieval-Augmented Generation) pipeline design and optimisation
Prompt engineering, evaluation frameworks, and red-teaming
Fine-tuning and RLHF for domain-specific models
Vector database selection and implementation (Pinecone, Weaviate, pgvector)
AI observability: tracing, eval metrics, drift detection

Who This Is For

Product teams adding AI features who lack ML depth in-house

Startups building AI-native products that need to move faster than hiring allows

Enterprises evaluating AI use cases and needing a proof-of-concept validated quickly

Common Questions

Do you work with open-source models or only commercial APIs?

Both. We have engineers with experience across the full spectrum — from GPT-4 and Claude to Llama 3, Mistral, and self-hosted inference on Ollama or vLLM. We recommend based on your latency, cost, and data-privacy requirements.

Can you help evaluate whether AI is the right solution for a use case?

Absolutely. We often start engagements with a 2-week discovery sprint that scopes feasibility, data requirements, cost projections, and a recommended approach before any build begins.

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