// INTELLIGENCE

Turn artificial intelligence into business capability.

We integrate AI into products and workflows where it can create measurable operational value.

AI ARCHITECTUREASSEMBLED
USER
APPLICATION
AI ORCHESTRATION
LLM
TOOLS
DATA
VECTOR DATABASE
// THE CHALLENGE

What companies come to us with.

  1. 01Pilots that impress in a demo but never reach production.
  2. 02Company knowledge locked in documents, tickets and email that no system can query.
  3. 03Uncertainty about data privacy, cost and reliability of language models.
// WHAT WE DELIVER

Capabilities

  • AI Strategy01
  • LLM Integration02
  • RAG Systems03
  • AI Agents04
  • Intelligent Automation05
  • Document Intelligence06
  • AI Search07
  • Data Pipelines08
  • Model Integration09
// ENGINEERING APPROACH

How Soft4Tech approaches it.

01

Use case before model

We start from a workflow with a measurable outcome, then choose the smallest model and architecture that serves it.

02

Grounded, not guessing

Retrieval over your own data, tool calling and validation layers keep outputs traceable and controllable.

03

Operated like software

Evaluation sets, monitoring and cost controls make AI features maintainable after launch.

// TECHNOLOGIES

Relevant stack

Chosen per project. These are the technologies we most often reach for in this area.

LLM APIsOpen-weight modelsVector databasesLangChain / LangGraphPythonFastAPIPostgreSQL + pgvectorElasticsearchKubernetes
// USE CASES

Where this applies.

01

Document intelligence

Extract, classify and validate data from contracts, invoices and forms.

02

Internal knowledge assistant

Answer employee questions from policies, manuals and tickets with citations.

03

AI agents in operations

Automate multi-step tasks across systems with human approval where it matters.

04

Intelligent search

Semantic product or content search that understands intent, not just keywords.

// RELATED WORK

Selected case studies

All work →
Document Intelligence PipelineSAMPLE CONTENT
PROFESSIONAL SERVICES

Document Intelligence Pipeline

Extracting structured data from contracts and forms with human review.

Explore Case Study →
// FAQ

Common questions

Can this run on our own infrastructure?

Yes. We deploy open-weight models on private cloud or on-premises where data residency requires it.

How do you control hallucinations?

Retrieval grounding, constrained outputs, validation steps and evaluation sets that are run on every change.

What does an AI project cost to run?

We design for cost from the start: model routing, caching and batch processing keep inference spend predictable.

// RELATED SERVICES

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