Computer vision and NLP development services

Computer vision covers models that work with images and video: detecting defects, reading labels, counting items or checking that a process step happened. Natural language processing (NLP) covers models that work with text: classifying messages, extracting fields from documents, summarising, or matching records.

Many real problems use both. Reading an invoice means finding the text regions in an image, then understanding what each field means. That is why we offer computer vision and NLP development as one service.

A project typically covers:

• Defining the task and the cases that matter most to get right
• Collecting, cleaning and labelling data, or planning how to
• Choosing and training or adapting the model
• Building an evaluation set that reflects real conditions
• Deploying to the cloud, on-premise servers or edge devices
• Integrating results into your existing systems

These services suit teams where people currently look at images or read text to make a repeated decision: quality inspectors on a production line, analysts reviewing documents, support staff sorting messages, or product teams that want to process user uploads automatically. The common thread is a task that is well understood by the people doing it, repeated often, and slowed down by manual review.

• Manufacturing. Visual inspection for surface defects, assembly checks, reading gauges and serial numbers, and monitoring for safety equipment. These systems often need to run on edge hardware near the production line, which shapes model choice from the start.
• SaaS. Image moderation, product photo tagging, and extracting structured data from screenshots or scanned uploads inside a product.
• Fintech. Identity document checks, reading scanned forms and statements, and detecting tampering in uploaded documents, with a review step for anything the model is unsure about.

NLP work is most useful where people currently read large volumes of text to find a few facts or make a routing decision.

• SaaS. Classifying support tickets, detecting intent in customer messages, and extracting entities from user content for search and reporting.
• Manufacturing. Structuring maintenance logs and incident reports so patterns can be searched and analysed, and matching supplier documents to orders.
• Fintech. Extracting terms from contracts, classifying transaction descriptions, and screening communications for compliance review.

For many of these tasks, a large language model is now the quickest way to a first version. For high-volume or latency-sensitive work, a smaller fine-tuned model is often cheaper to run. See our custom LLM development page for how we make that choice.

We choose models for the constraints of the job rather than for novelty.

For vision, convolutional networks remain a strong choice for fast detection and classification on edge hardware. Vision transformers and large pretrained vision models suit tasks with limited labelled data or high variation in what the camera sees, because they transfer well from general training. Multimodal models that take both images and text are useful for documents and for describing what is in a scene.

For text, the choice is usually between prompting a large language model, fine-tuning a smaller one, or a classical approach for simple, stable tasks. We compare them on your evaluation set before committing.

Where a model runs affects its design as much as the task does. Cloud deployment is simplest to scale and update, and suits most document and text workloads. On-premise deployment keeps images and documents inside your network, which matters for sensitive data and some regulated environments. Edge deployment runs the model on hardware next to the camera or machine, which avoids network delays and keeps working if the connection drops, but limits model size.

We agree the deployment target during assessment, because it determines which models are realistic, how updates are rolled out and how monitoring data is collected.

The quality of a computer vision or NLP model depends more on its data than on its architecture. Most of our early effort goes into making sure the training and test data match real conditions: lighting, camera angles, document layouts, languages and the rare cases that matter most.

Evaluation is agreed with you up front, including which errors are costly. Missing a defect and flagging a good part carry different costs, and the model is tuned to reflect that. In production, low-confidence predictions go to a person, and monitoring watches for drift when inputs change, such as a new camera or a new document template.

Choose a partner who asks about your data and your deployment environment before talking about models. The model is rarely the hard part. Getting representative data, agreeing what counts as an error, and fitting the result into an existing line or workflow usually are.

Questions worth asking:

• How will you test the model on conditions that differ from the training data, such as new lighting, new suppliers or new document layouts?
• Which errors will the model be tuned to avoid, and who decides?
• What happens to predictions the model is unsure about?
• What will our team need to retrain or recalibrate the model after handover?

If the answers are vague, the project is likely to work in a demo and struggle in production.

A project is not ready if there is no way to collect representative examples, or if nobody can say what a correct result looks like. It may also be the wrong tool if a simpler sensor, barcode, form field or rule would solve the problem more reliably. We check for these during assessment and will recommend the simpler route when it exists.