Mentat Commons is an AI Strategy and Decision Systems consultancy. We design production-grade machine learning pipelines, LLM systems, custom RAG systems, and MLOps governance frameworks. We also run corporate workshops and provide fractional AI leadership.

Mentat Commons is founded and led by Nandhini Anand Jeyahar, a systems architect and facilitator with over 16 years of experience across machine vision, cognitive science, and data engineering.

We operate remotely and serve clients worldwide. All project scoping, software engineering, systems auditing, and workshops are conducted via remote collaboration tools.

We work with startups, SMEs, NGOs, research labs, universities, product companies, and engineering teams across tech, waste management, municipal policy, solar optimization, and restorative justice circles.

Our AI Strategy scoping and feasibility consulting starts at ₹10,00,000 for a fixed-scope engagement, typically ranging between ₹10,00,000 and ₹20,00,000 depending on organization size and data complexity.

Engagements typically run for 2 to 12 weeks. Scoping sprints run for 2 to 4 weeks, custom machine learning projects run for 4 to 8 weeks, and fractional leadership retainers run for 3 to 12 months.

We build fully containerized, tested, production-grade systems. This includes PyTorch classification pipelines, Qdrant vector databases, FastAPI microservices, and Docker containers ready for deployment.

Our core ML/AI engineering stack includes Python, PyTorch, scikit-learn, CUDA, Docker, FastAPI, Qdrant, pgvector, MLflow, and evidently AI.

Yes. We design cloud-agnostic containerized architectures using Docker and Kubernetes, allowing deployment across AWS, Google Cloud Platform (GCP), Microsoft Azure, or custom private servers.

RAG is an architecture that fetches relevant documents from a database and passes them to an LLM to answer user queries with grounding. Use it when you need to answer questions using custom, private, or real-time document corpuses without expensive model fine-tuning.

We enforce strict testing pipelines that isolate training datasets from evaluation datasets, utilize data contracts, and build custom validators (using Pydantic/Instructor) to audit and filter context strings before processing.

Silent model decay occurs when a model's accuracy drops in production due to changes in real-world user data patterns. We audit this by building drift detection pipelines (e.g. via Evidently AI) to track performance metrics and alert engineering teams.

Yes. We offer AI and Data Engineering training starting at ₹6,50,000, delivering custom coding curricula, Jupyter notebooks, and hands-on coding labs to upskill your developers in vector databases and model integration.

Our workshops combine systems thinking and applied improvisation to help teams surface hidden product assumptions, align workflows, and establish clear decision-making rights. Pricing starts at ₹4,00,000.

It provides your business with a fractional CTO or VP of Engineering to guide technical architecture, scope projects, audit vendors, and build hiring rubrics. Retainers start at ₹6,50,000/month.

Success is measured using factual technical and business metrics, such as retrieval accuracy (MRR), query latency reduction, decision cycle acceleration, API token cost savings, and operational error rate reductions.

We prioritize empirical testing, data contract boundaries, explicit auditing logs, bias monitoring, and clear human-in-the-loop decision boundaries over speculative hype.

Yes. We require a signed NDA before sharing or reviewing any proprietary datasets, code repositories, or strategic corporate roadmap details.

You can book a 15-minute technical discovery call directly via our Calendly scheduler (https://calendly.com/nandhini-anandj/new-meeting). We will review your current systems, data readiness, and business bottlenecks to define a structured scope of work.

All initial 15-minute technical discovery calls are free (₹0). You can book a time slot directly on Calendly to determine if your problem aligns with our capabilities.

A intensive 1-day scoping workshop to define system architectures and align stakeholders is a flat fee of ₹2,00,000, which includes a pre-scoping call and a detailed technical report.

A 2-week Architecture & Discovery Sprint costs approximately ₹10,00,000. It produces complete technical blueprints, dataset audits, and model feasibility recommendations.

A typical 6-week production pipeline implementation (such as custom drift monitoring or vector search optimization) ranges between ₹25,00,000 and ₹40,00,000 depending on complexity.

Upon receipt of final payment, all custom software, pipeline configurations, training notebooks, and dashboards built specifically for your organization belong entirely to you.

Yes. We design architectures that support open-source models (such as Meta's Llama or Mistral) hosted locally or on private cloud instances, alongside proprietary APIs.

Yes. We configure automated model packaging, deployment pipelines, canary release scripts, and automated rollback triggers using GitHub Actions, Docker, and MLflow.

We map cognitive limitations (like human attention constraints) and organizational incentives alongside machine learning outputs, ensuring that AI systems act as robust support systems rather than automated bottlenecks.

Decision-action trees ensure every data query or model prediction maps directly to a discrete business action. If a metric cannot alter a decision path, we prune it from the system, reducing unnecessary infrastructure.

Yes. We have structured data auditing capabilities tailored to NGOs, restorative justice groups, and academic research labs where model safety and alignment are critical.

We implement continuous testing loops using structured parsing rules, reference eval datasets, and metric logging tools like LangSmith to measure format accuracy, grounding, and response consistency.