Agents & copilots
Intelligent assistants that operate inside your team's workflow, automating repetitive decisions and amplifying human capacity without replacing oversight.
- LLMs
- RAG
- Function calling
- LangChain
Applied AI engineering
Artificial intelligence that
solves real problems.
We build solutions that deliver real results.
Principles that guide every project.
People at the core. Technology in service. Excellence as standard.
We build software with embedded AI for companies that treat technology as an operational tool — not as a trend. Our work starts by understanding people and process, and ends in systems that generate results.
— IA Corps
What we deliver
Four areas we combine to solve real operational problems — not boxed packages, not slide-deck promises.
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Intelligent process automation
Pipelines that combine business rules, classification models, and on-demand human review to solve tasks that used to take hours of manual work.
- OCR
- Supervised classification
- Workflow engines
- Postgres
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Computer vision
Detection, classification, and visual analysis models applied to inspection, quality control, and real-time operation monitoring.
- YOLO
- PyTorch
- ONNX Runtime
- Edge deployment
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Technical discovery
Short validation sprints and proofs of concept that rigorously answer whether an AI idea is viable, worth the investment, and how it should be built.
- Feasibility analysis
- Iterative POC
- Benchmarks
- Total cost
Industries served
Industry Logistics Finance Healthcare Retail Industry Logistics Finance Healthcare Retail Industry Logistics Finance Healthcare Retail
- 12+
- Projects shipped
- 4
- Industries served
- 100%
- In production
How we work.
Four phases — from question to continuous operation. No shortcuts. No deliver-and-vanish.
Phase 01
We understand the problem, operational context, and success criteria before any code. The output is a well-formed problem document that guides everything else.
- Current process map and bottlenecks
- Quantitative success criteria
- Prioritized technical hypotheses
Duration 2 to 3 days
Phase 02
We build a prototype focused on the riskiest hypothesis. The goal is to answer "does this work?" before investing in production, with real benchmarks and cost.
- Functional prototype in controlled environment
- Benchmark with real data
- Total cost estimate
- Technical recommendation to proceed or pivot
Duration 4 to 5 days
Phase 03
We build the complete solution, integrated into your environment, with test coverage, observability, and human-review processes where operations require it.
- Application in production
- Test coverage and monitoring
- Technical and operational documentation
- Internal team training
Duration 5 to 7 days
Phase 04
We monitor the solution in production, tune models based on real data, and establish a continuous improvement cycle. You're not alone after deploy.
- Ongoing technical support
- Model reassessment each cycle
- Shared evolution roadmap
- Documented SLAs
Duration Ongoing