AI and machine learning built around a problem you can measure
We build AI that earns its place: models, agents and vision systems tied to a cost, a risk or a cycle time you already track, and proven against a measured baseline.
Capabilities
AI and machine learning services we deliver
Every engagement starts with your data and a measured baseline. When a model proves its value against that baseline, it runs inside the systems your teams already use, on cloud or private infrastructure. Most projects combine several of these: a quality-inspection system, for example, needs a vision model, edge deployment, monitoring and an integration that records each rejection in your ERP.
Predictive analytics and forecasting
Demand, failure, churn and cash-flow models built on your historical data, with clear accuracy measures and the main drivers behind each prediction.
Computer vision
Detection, classification, counting, measurement and OCR from fixed cameras and mobile devices, optimised to run on edge hardware close to the process.
AI agents and assistants
Assistants that answer from your documents and act in your systems through controlled tool access, with permissions, logging and human approval for sensitive steps.
Intelligent document processing
Extraction and validation of invoices, claims, forms and contracts, including scans, with confidence scores that route uncertain cases to a person.
Private and in-country AI
Open-weight language and vision models hosted in your data centre, a private cloud tenancy or with in-country providers, so sensitive data stays under your control.
MLOps and model governance
Versioned data and models, retraining pipelines, drift monitoring and audit trails, so models stay accurate after go-live and decisions can be explained.
Integration with business systems
Connecting models to ERP, CRM, data warehouses and line-of-business applications through APIs and event streams, so predictions reach the people who act on them.
Not sure which capability you need?
Describe the problem and the systems involved. We will suggest where AI and machine learning can help, what else the work needs and a sensible first step.
Use cases
Where AI and machine learning can help
Typical problems this capability is used to solve. Each one starts from a cost, risk or delay you can measure.
Demand forecasting for distribution and retail
SKU and location-level forecasts that feed replenishment and production plans, replacing spreadsheet extrapolation and gut feel.
Visual inspection on a production line
Camera checks for surface defects, missing components or label errors, with every rejection image stored against batch and shift for traceability.
Policy and procedure assistant
An internal assistant that answers staff questions from approved manuals, policies and product documents, citing the source for every answer.
Claims and invoice extraction
Automatic capture of fields from claims and supplier invoices, checked against master data before anything is posted to ERP.
Deliverables and technology
What you receive and what we build with
What you receive
The typical outputs of an engagement, scaled to the size of the work.
- Problem statement and success metrics agreed with the business owner, with a measured baseline
- Data assessment: what exists, its quality, the gaps and any labelling effort required
- Trained models with evaluation reports on held-out data, including known limitations
- Production deployment as APIs, batch jobs or edge services, integrated with your systems
- Monitoring for accuracy, drift, latency and usage
- Source code, model weights, documentation and handover sessions for your team
- A retraining and support plan, run by us or by your team
Technologies we work with
Chosen to fit your existing systems, skills and hosting rules.
- Python
- PyTorch
- scikit-learn
- XGBoost and LightGBM
- OpenCV
- ONNX Runtime
- NVIDIA TensorRT and Jetson
- Hugging Face Transformers
- Open-weight LLMs (Llama, Mistral, Qwen)
- Commercial LLM APIs
- PostgreSQL with pgvector
- MLflow
- FastAPI
- Docker and Kubernetes
- Azure, AWS and GCP
- On-premises and in-country hosting
Approach
How an engagement runs
New problems usually start with a short paid diagnostic and a paid pilot, so you can judge results before committing further. Well-defined work, such as a website or a portal, starts as a fixed-scope project. Live systems can move to a retainer or a managed service. Compare engagement models.
Step 1: Frame the problem
We agree the decision the model will support, how success will be measured and what today's baseline is. If AI is not the right tool, we say so.
Step 2: Assess the data
We profile your data, test whether it can support the target accuracy and plan any labelling or additional data capture.
Step 3: Pilot against the baseline
A paid pilot, typically 8–16 weeks, runs the model on real data alongside the current process so the results can be compared fairly.
Step 4: Deploy and operate
We harden the model for production, integrate it, set up monitoring and either hand over to your team or run it as a managed service.
FAQs
Questions about AI and machine learning
Something else to ask?
Email your question with a little about the systems involved, and we will reply in plain language.
info@tashit.comDo we need a large dataset before we start?
Not always. Many useful forecasting models start from one to two years of transaction history, and a first vision model can often be trained on a few hundred labelled examples per defect type, then improved. The data assessment tells you what is realistic before you commit to a pilot.
Can our data stay in our country or on our own servers?
Yes. We can deploy open-weight models on your own hardware, in a private cloud tenancy or with in-country hosting providers. Data-residency and regulatory requirements shape the architecture from the first design session.
How do you keep generative AI accurate and safe?
We ground assistants in your approved sources, show citations, restrict actions to defined tools and test against a set of real questions before launch. High-risk actions need human approval, and every interaction is logged for review.
Who owns the models and the code?
Ownership is agreed in the contract before work starts. A common arrangement is that you own the source code, trained model weights and documentation produced for you. Open-source libraries and third-party components keep their own licences.
Planning work in AI and machine learning?
Send us the brief: what you want to build or improve and the systems it has to work with. We will reply with a first step, a plan and a timeline.