AI & Machine Learning

Useful AI,not a demo ona stage.

We embed LLMs and retrieval into products people already use. Cost caps, evaluation harnesses, fallback models, and the boring observability that turns a clever demo into something a regulated team can actually deploy.

AI and machine learning concept — neural networks
Capabilities

Four practices, one AI team.

01

LLM integration

GPT-4oClaudeGemini

We embed large language models where they actually add value — not as demos, but as production features users depend on.

  • Prompt engineering & system design
  • Multi-model routing & fallback
  • Streaming responses & SSE
  • Cost optimisation & caching
  • Structured output & JSON mode
  • Function / tool calling
02

RAG & knowledge bases

EmbeddingsVector DBRetrieval

Ground your AI in your own data. We design retrieval pipelines that keep answers accurate, current, and auditable.

  • Document ingestion & chunking
  • Embedding models (OpenAI, Cohere)
  • Vector databases (Pinecone, Weaviate, pgvector)
  • Hybrid search (dense + sparse)
  • Re-ranking & contextual compression
  • Evaluation & hallucination testing
03

AI agents

OrchestrationToolsMemory

Autonomous workflows that reason, plan, and act — connected to your APIs, databases, and external services.

  • ReAct & plan-and-execute agents
  • Tool / function orchestration
  • Long-term memory stores
  • Multi-agent systems
  • Human-in-the-loop checkpoints
  • Tracing & observability
04

Data pipelines

ETLAnalyticsDashboards

Clean, reliable data is the foundation of every AI system. We build pipelines that flow from raw source to production model.

  • ETL & event streaming
  • Data warehouse integration
  • Feature engineering
  • Analytics dashboards
  • Real-time data pipelines
  • Data quality monitoring
Use cases

AI that does real work.

Use case

Legal document review

Extract clauses, flag risks, and summarise contracts in seconds rather than hours.

Use case

Customer support copilot

AI that drafts replies, routes tickets, and escalates edge cases to the right human.

Use case

Internal knowledge assistant

Ask anything about your docs, wikis, and runbooks — get cited, accurate answers.

Use case

Data extraction & classification

Turn unstructured PDFs, emails, and forms into structured records at scale.

Use case

AI-powered onboarding

Guide new users to their "aha moment" with personalised, conversational flows.

Use case

Predictive analytics

Churn prediction, demand forecasting, anomaly detection — built on your own data.

Technology

Our AI stack.

We're model-agnostic and cloud-agnostic — we pick the right tool for your use case, not the one we're most familiar with.

OpenAI
LLM
Anthropic
LLM
LangChain
Orchestration
LlamaIndex
RAG
Pinecone
Vector DB
pgvector
Vector DB
Weaviate
Vector DB
Vercel AI
SDK
LangSmith
Observability
Python
Language
How we work

From idea to production.

01
Discovery & scoping
We map your data, users, and workflows to find where AI creates real leverage — not just novelty.
02
Prototype & evaluate
Two-week spike: we build a working prototype, measure output quality, and stress-test edge cases before committing.
03
Build & integrate
Production implementation wired into your product — with streaming, error handling, and cost guardrails built in.
04
Monitor & improve
We instrument every AI call, track quality over time, and ship improvements as models and your data evolve.
Let's build something intelligent

Got an AI idea worth shipping?

info@satvixtech.com