What We Deliver
Tailored services designed to accelerate outcomes in your domain
Custom ML Models
Classification, regression, and time-series models tailored to your datasets and KPIs.
NLP & LLM Apps
Question-answering, RAG pipelines, chatbots, and summarization with domain grounding.
Computer Vision
Detection, OCR, defect inspection, and video analytics for real-world environments.
Recommendations
Personalization, ranking, and next-best-action systems for growth.
Forecasting
Demand, supply, and financial forecasting with uncertainty quantification.
MLOps
Feature stores, experiment tracking, CI/CD for models, and scalable model serving.
How We Engage
We design, build, and productionize AI systems that are grounded in your data and tied to measurable business value.
Our approach emphasizes evaluation, governance, and lifecycle management to ensure safe, reliable AI at scale.
Where This Shines
RAG-powered assistants for customer support and internal knowledge
Computer vision for quality inspection, OCR, and content moderation
Personalization and recommendation engines to boost engagement
Forecasting for demand, supply, and financial planning
What You Get
- Model cards, evaluation reports, and bias/safety assessments
- Reusable pipelines: feature store, training, and serving
- Observability dashboards for drift, quality, and costs
- Playbooks for human-in-the-loop review and remediation
Ways We Work Together
Choose the model that best fits your goals, timelines, and team capacity.
Discovery & POV
Rapid experiments to validate feasibility, data fit, and value hypotheses.
End-to-End Delivery
From data to production with evaluations, governance, and handover.
Platform Enablement
MLOps platform buildouts, best practices, and enablement workshops.
Our Approach
Frame
Define the problem, value metrics, and guardrails with stakeholders.
Experiment
Explore baselines, architectures, and data readiness.
Build
Train, evaluate, and iterate with robust testing and monitoring.
Deploy
Automate CI/CD for models, roll out safely with controls.
Operate
Monitor drift, costs, and quality; retrain and improve.
Measuring Success
Business KPI lift (conversion, retention, AHT reduction)
Model quality (precision/recall, RMSE, win rate vs baseline)
Latency, availability, and cost per request
Drift and intervention rate in production
Typical Engagement Timeline
Discovery
2–3 weeks
Value, data audit, baselines
Build
4–8 weeks
Modeling, evaluation, pipelines
Productionize
3–6 weeks
Serving, monitoring, handover