AI Development
AI Development Services for Production-Ready Products
We build AI systems that run in production, not demos. LLM applications, RAG pipelines, copilots and AI integrations, engineered with the retrieval quality, evaluation, and monitoring that separate a working prototype from a system your team can trust.
What ai development
AI development, as Algorimsoft delivers it, is the engineering of AI-powered features into real products: connecting large language models to your data and tools, building retrieval-augmented generation (RAG) over your documents, and shipping the evaluation and monitoring layer needed to know whether the system is actually working after launch.
Business problems this solves
- A chatbot or AI feature works in a demo but breaks or hallucinates on real customer questions
- Internal knowledge (docs, tickets, CRM notes) is siloed and impossible to search or reason over
- There's no way to measure whether an AI feature is actually improving after it ships
- Existing AI experiments were built by a vendor or contractor with no path to production
- Leadership wants an AI roadmap but the internal team lacks LLM engineering experience
Capabilities
LLM application development
Product features built directly on top of LLM APIs — from copilots to structured extraction and classification.
RAG (retrieval-augmented generation)
Ingestion, chunking, embeddings, and retrieval over your documents, tickets, or CRM data, with citations.
AI integrations
Connecting AI features into your existing product, CRM, helpdesk, or internal tools via API.
Custom AI systems & copilots
Purpose-built assistants for a specific workflow — support, sales research, internal knowledge — instead of a generic chatbot.
Evaluation & monitoring
Test sets, retrieval quality checks, and production monitoring so you can see when an AI feature drifts or fails.
How we build it
- 01
Use case definition
Identify the specific workflow the AI system needs to improve, and what a good answer looks like.
- 02
Data review
Assess the source documents, tickets, or data the system will retrieve from or learn about.
- 03
Prototype
Build a working version against real data early, rather than a slide deck.
- 04
Evaluation
Build a test set and measure retrieval and answer quality before wider rollout.
- 05
Production deployment
Ship with monitoring, fallback behavior, and human-review paths where needed.
Technologies
- OpenAI, Anthropic & Google Gemini APIs
- Vector databases (pgvector, Pinecone, Qdrant)
- LangChain / LangGraph
- Python & TypeScript
- Evaluation tooling (custom test sets, LLM-as-judge with human review)
Use cases
- — Internal knowledge assistant answering questions from company docs, wikis, and tickets
- — Customer support copilot drafting responses from help-center content and history
- — Structured data extraction from documents, forms, or emails
- — AI feature embedded directly into an existing product's UI
Why Algorimsoft
- — AI features are engineered with evaluation and monitoring from the start, not bolted on after launch
- — Retrieval quality is treated as the deciding factor in output quality, not an afterthought behind the model choice
- — The same team can extend an AI feature into a full agentic workflow (see AI agent development) as the use case matures
Engagement options
Focused pilot
A scoped AI feature or RAG system built against a single, well-defined use case.
Embedded AI engineer
An AI engineer working inside your existing product team on an ongoing basis.
Full build & launch
End-to-end delivery of an AI-powered feature or product, from data pipeline to production monitoring.
Frequently asked questions
Ready to talk about ai development?
Tell us about your product, timeline, and team, and we'll follow up with next steps.