Founder-led AI engineering · India → Global
Asthra AI Labs helps ambitious teams design, train, evaluate and deploy domain-specific language models, agentic workflows, retrieval systems and production-grade machine learning infrastructure.
Senior engineering involvement from discovery through deployment.
Inputs
Domain data · Base model · Requirements
Model Adaptation
Data prep · CPT · SFT · Efficient adaptation
Evaluation & Feedback
Correctness · Reliability
Adapted Model
Validated checkpoint
Agent / API Integration
Tools · Memory · Serving
Evaluation ··· iterate ···→ Adaptation
Positioning
Complex domains demand more than an API wrapper. They require careful data design, domain adaptation, evaluation, systems engineering and an understanding of how the model will operate inside real workflows.
From first technical assessment to a working production path.
Continued pretraining, supervised fine-tuning, synthetic-data pipelines and domain adaptation for specialist technical or business environments.
Evaluation systems that measure task performance, correctness, failure modes and operational behaviour—not merely training loss.
Tool-using AI workflows that combine models, memory, retrieval, structured execution and human control.
Retrieval systems for organisations that need grounded access to technical, operational or proprietary knowledge.
Training, deployment and monitoring infrastructure designed around practical constraints.
DOMAIN LLM SYSTEMS
Training architecture, domain data preparation, parameter-efficient adaptation and functional evaluation planning for specialised RTL and hardware-design tasks.
ML PERFORMANCE
Performance engineering for clustering-visualisation methods, reducing compute overhead while preserving analytical usefulness.
APPLIED ML
Classification, feedback capture, confidence-aware routing and retraining architecture for practical email organisation.
Define the real problem, constraints, success criteria and existing technical environment.
Evaluate data, models, infrastructure, risks and the most efficient path to evidence.
Implement the smallest technically meaningful system rather than a disposable demonstration.
Measure task performance, failure modes, cost, latency and operational reliability.
Connect the system to real workflows, monitoring and human decision points.
Engagements can begin with a focused technical assessment, proof of concept or scoped engineering workstream.
Founder
Principal engineer
Avinash Mynampati
Founder & Principal AI Engineer
India → Global
Founder & Principal AI Engineer
Avinash is an AI and machine-learning engineer working across domain-specific language models, applied ML systems, training infrastructure and research prototyping. His professional experience includes roles at Juspay and Codingmart, alongside current work on specialised language-model systems for semiconductor engineering.
He founded Asthra AI Labs to work directly with teams tackling technically difficult problems—combining research judgement, engineering execution and meaningful evaluation from the earliest stage of a project.
A convincing demo is not the same as a dependable system. We design evaluation into the work from the beginning.
Specialist workflows require specialist data, language, constraints and measures of correctness.
We prioritise evidence and reusable foundations over broad but fragile prototypes.
Senior technical involvement continues through implementation, debugging and iteration.
Privacy-conscious email intelligence using classification, confidence-aware routing, feedback and retraining workflows.
Private exploration
Persistent, local-first development assistance with project context, checkpoints, error memory and agentic execution.
Research prototype
High-performance implementations of Visual Assessment of Tendency methods for exploratory cluster analysis.
Research engineering
Labs initiatives are exploratory. Consulting and engineering execution remain the core of Asthra AI Labs. Learn more
Training loss is a weak proxy for specialist correctness. Domain systems need task-level evaluation, failure taxonomies and operational checks before they are treated as dependable.
Read articleNot every domain problem needs continued pretraining. A practical framework for choosing between data exposure, instruction tuning and lighter adaptation methods.
Read articleRetries are not a recovery strategy. Reliable agents need state, checkpoints, tool contracts and explicit paths for partial failure.
Read articleNext step
Bring the domain, constraints and current system. We will help determine what is feasible, what should be measured and what is worth building.
Initial conversations are exploratory and confidential.