Custom AI Systems
AI engineered into the software your business runs on.
We build custom AI as part of complete applications — software that reads your documents, applies your rules, supports your decisions and keeps people in control where accuracy matters.
Our position
AI where it earns its place.
Not every problem needs a model. We start with the process, the data and the cost of an error, and use AI where it clearly outperforms rules, search or a better interface. Where it doesn't, we build conventional software that does the job reliably.
Capabilities
What we build with AI
- 01
Tailored intelligent software
Applications designed around a specific task — classifying, extracting, matching or recommending — using your data and your definition of a correct result.
- Task-specific models and prompts
- Evaluation on your real data
- Defined behavior when the model is unsure
- 02
AI inside larger applications
AI as one component of a complete system, with the backend, permissions, interfaces and integrations it needs to be useful every day.
- Embedded in existing workflows
- Role-based access to AI features
- Connected to your systems of record
- 03
Document & engineering-data intelligence
Turning drawings, specifications, certificates, forms and other technical documents into structured, validated data.
- Drawings, PDFs and scanned documents
- Field-level validation rules
- Traceability back to the source
- 04
AI-supported decision systems
Tools that gather the relevant data, apply your business logic and present recommendations — while the decision stays with your people.
- Explainable recommendations
- Comparison of alternatives
- Decision history and audit trail
- 05
Human-in-the-loop systems
Review and approval steps designed into the workflow, so experts spend their time on exactly the results that need attention.
- Confidence-based routing
- Fast, focused review interfaces
- Corrections that improve the system
- 06
Complex AI software architecture
Architecture for systems in which several models, data sources and services must work together dependably.
- Model-independent service design
- Cost, latency and quality control
- Versioning and monitoring
Architecture
Anatomy of a production AI system
A dependable AI feature is mostly engineering around the model. This is the structure we typically design.
- 01
Sources
Documents, drawings, databases, user input
- 02
Ingestion
Parsing, normalization, OCR
- 03
Model layer
Extraction, classification, reasoning
- 04
Validation
Business rules and confidence thresholds
- 05
Human review
Experts approve or correct flagged results
- 06
Systems of record
ERP, databases and downstream workflows
Reviewer corrections feed evaluation and improvement
Production readiness
What separates a demo from a dependable AI system
- 01
Evaluation on real data
Accuracy is measured on representative samples from your operation before anything reaches production.
- 02
Confidence and fallbacks
Every output has a defined path when the model is uncertain: a rule, a retry or a person.
- 03
Traceability
Results link back to the source document, the model version and the reviewer, so decisions can be audited.
- 04
Data handling
The architecture follows your data requirements, including the choice between hosted model APIs and private deployment.
- 05
Cost and latency control
Caching, batching and model selection keep running costs and response times predictable.
- 06
Monitoring
Quality is tracked over time, so changes in data or model behavior are detected early.
Frequently asked questions
Do we actually need AI for this?
Sometimes not. Part of our early work is determining whether AI adds real value compared with rules, better data structure or a better interface. If it doesn't, we'll say so and propose the simpler solution.
Can AI work with our existing ERP and databases?
Yes — integration is usually the core of the work. AI components read from and write to your existing systems through defined interfaces, with validation in between.
How do you handle errors and accuracy?
We measure accuracy on your data, set confidence thresholds and design review steps for the cases that need them. The goal is predictable behavior, not a system that is right most of the time and silently wrong otherwise.
Where does our data go?
That is an architectural decision we make with you at the start, based on your security and compliance requirements. Options range from hosted model APIs under appropriate terms to private deployments.
Start a conversation
Have a system that off-the-shelf software can't handle?
Describe the problem. We'll reply with an engineering perspective on how it could be solved and what a sensible first step looks like.
Or write to contact@shama-tech.com