AI & ML development services

Generative AI reduces the cost of producing content, documentation, and training data to a fraction of today’s costs. For businesses running daily content-heavy operations, such as legal, healthcare, financial services, or e-commerce, that’s a significant improvement.

To help you gain maximum value from this AI & ML development service, we build generative solutions fine-tuned on your data – contracts, product catalogs, and internal documentation. Generic models tend to hallucinate terminology and misread regulatory context. A model trained on your material produces output that is accurate enough to use directly.

You can forget about proofreading every document before sending it.

Use cases: Automated report and document drafting, Marketing copy at scale, Synthetic training data generation, Internal knowledge base automation, Personalized customer communications, RFP and proposal automation.

Technologies: OpenAI API, Anthropic API, LangChain, Hugging Face, LlamaIndex.

Industries: Media, E-Commerce, Legal Tech, Healthcare Documentation, HR Tech, Financial Services.

Deploying a model is not a finish line – it’s a milestone. Without the right operational infrastructure, accuracy gradually erodes without anybody noticing. Until a specific business decision brings the problem to the surface.

Our MLOps services build the layer that keeps models working after launch. It includes continuous integration and delivery pipelines, automated retraining, drift detection, performance monitoring, and rollback protocols. This way, your data science team gets a faster path from experiment to production, and your operations team gets a reliable system.

Track your model’s performance on a dashboard, rather than discovering that something has been slipping when it’s already too late.

Use cases: Continuous model retraining, Experiment tracking, Feature store management, Production performance monitoring, Staged rollout, and A/B testing of model versions.

Technologies: MLflow, Kubeflow, Apache Airflow, Docker, Kubernetes, Azure ML, Vertex AI, AWS SageMaker.

Industries: Finance, SaaS platforms, Logistics, E-Commerce, Manufacturing, Healthcare.

Computer Vision makes decisions from visual data – at a speed and consistency that no manual process can match. Examples include defect detection running at full production line throughput, medical imaging analysis that flags anomalies before a clinician opens the file, and object recognition across a warehouse floor without a single manual scan.

We build CV solutions tailored to your environment, hardware configuration, lighting conditions, defect taxonomy, and throughput requirements. Cases vary from object detection and facial recognition to image classification and augmented reality.

When the system goes into production, you can trust it completely. It is trained on your data, tested in your conditions, and tuned to your tolerances.

Use cases: Automated quality control, Retail shelf monitoring, Medical image analysis, Security surveillance, Document digitization and OCR, AR applications, Vehicle and object tracking.

Technologies: OpenCV, YOLO, PyTorch, TensorFlow, Detectron2.

Industries: Manufacturing, Healthcare, Retail, Oil&Gas, Logistics, Construction.

User behavior analytics

Your users inform you of their intentions long before they act. The signals hide in session patterns, feature usage, support interactions, and browsing sequences. Are you sure that your current systems are reading those signals early enough to act on them?

We build predictive behavior models that identify churn risk before cancellation, surface conversion signals before the decision, and flag disengagement before it becomes absence.

The insights your team gets are specific enough to act on. They can be a basis for strategizing, sales, or marketing activities.

Use cases: Churn prediction and early intervention, Recommendation engines, Funnel drop-off analysis, Cohort segmentation, Personalized onboarding flows, A/B test prioritization, Lifetime value modeling.

Technologies: Scikit-learn, XGBoost, Python, Apache Spark, dbt.

Industries: SaaS, E-commerce, Fintech, Edtech, Gaming, Subscription media.

Forecasting is only useful when it’s specific enough to inform a decision. Category-level, monthly, directional outputs look rigorous, but don’t provide much value. They’re compatible with too many outcomes to tell you what to actually do.

We build forecasting models that produce the granularity your teams need. SKU-level demand by region, equipment failure probability by individual asset, staffing requirements by shift and location. Outputs with confidence intervals and enough transparency that the person using the forecast understands what the model is responding to and knows when to trust it less.

Our goal is a forecast that your operations team actually uses, not one that looks good in the presentation and gets set aside afterward.

Use cases: Demand forecasting, Predictive maintenance scheduling, Dynamic pricing optimization, Financial modeling, Workforce planning, Inventory optimization, Energy load forecasting.

Technologies: TensorFlow, PyTorch, Scikit-learn, XGBoost, Apache Airflow.

Industries: Manufacturing, Retail, Energy, Logistics, Finance, Healthcare, Utilities.

Early detection and resolution of risks, fraud, and operational failures start with a system that analyzes the right data at the right granularity. Moreover, it should be able to do it fast.

The data that would have caught the problem is almost always there. A fraudulent transaction left a pattern. The failing component was definitely reported in the sensor logs.

We build anomaly detection systems that establish dynamic baselines accounting for seasonality, shift patterns, and operational variance, and surface deviations the moment they become statistically significant. Your teams get alerts about what deviated, by how much, against what baseline, with what confidence.

You will always have enough context to act immediately.

Use cases: fraud detection, network intrusion detection, predictive maintenance alerts, IoT sensor monitoring, financial transaction screening, quality control deviation flagging, and cybersecurity threat detection.

Technologies: Scikit-learn, PyTorch, Keras, Apache Spark, Kubernetes.

Industries: Finance, Cybersecurity, Healthcare, Industrial IoT, Insurance, Manufacturing, Utilities.

Most business-critical information lives in text. Unstructured and inconsistent contracts, support tickets, clinical notes, compliance filings, and call transcripts are expensive to process manually at any meaningful scale.

NLP makes that text readable to your systems. It classifies, extracts, summarizes, routes, and flags every document without a human in the loop.

We fine-tune the system to your domain-specific needs. It’s also trained on your documents. We don’t accept the assumption that a general-purpose LM will understand your indemnification clauses or diagnostic coding conventions. Or the way, your compliance team writes exception reports.

Your teams can fully rely on the system for such high accuracy.

Use cases: Contract analysis and risk extraction, Customer feedback classification, Intelligent enterprise search, Document summarisation, Chatbot intent recognition, Compliance screening, Clinical note processing.

Technologies: spaCy, BERT, Transformers, NLTK, Hugging Face, LangChain.

Industries: Legal tech, Insurance, Banking, Healthcare, HR Tech, Government, Financial services.