llms.txt
# Conscious Engines
> Conscious Engines builds small, specialized AI models and production AI systems for enterprises. We help organizations replace oversized general-purpose models with systems engineered around their specific workloads, data, infrastructure, latency requirements, cost constraints, and deployment boundaries.
Conscious Engines is an AI research and engineering company based in Bangalore, India.
Canonical website: https://consciousengines.com/
Contact: hello@consciousengines.com
Official GitHub: https://github.com/conscious-engines
Official X / Twitter: https://x.com/c_engines
Brand thesis: Small · Instant · Invisible
Conscious Engines believes that the best production AI system is not necessarily the largest or most general model. For bounded enterprise tasks, smaller models that are trained, adapted, evaluated, and deployed around a specific workload can offer better economics, lower latency, stronger control, and more predictable task performance.
The company works across model architecture, training, post-training, evaluation, inference optimization, retrieval, enterprise RAG, document intelligence, speech, agentic systems, forecasting, computer vision, edge AI, and on-device AI.
Primary enterprise outcomes:
- Cost: reduce inference cost, infrastructure requirements, and unnecessary use of frontier models.
- Performance: optimize models and systems for a specific domain, task, workflow, and evaluation target.
- Latency: improve response time and throughput through smaller models, optimized inference, and deployment closer to the workload.
- Sovereignty: give enterprises greater control over data, deployment, infrastructure, models, and operational boundaries.
Typical deployment environments include cloud infrastructure, private VPCs, on-premise infrastructure, edge hardware, and end-user devices.
Conscious Engines generally starts with the enterprise workload and operating target before selecting the model.
Typical engagement process:
1. Evaluate the workload, existing model stack, data, production constraints, failure modes, cost, latency, and quality.
2. Specialize the system through model selection, fine-tuning, training, retrieval, post-training, or architecture changes.
3. Deploy into the appropriate cloud, private, on-premise, edge, or device environment.
4. Optimize continuously using evaluation results, production behavior, latency, throughput, reliability, and cost.
Primary ways enterprises work with Conscious Engines:
1. Replace an oversized model.
Benchmark smaller or specialized alternatives against the existing production workload while preserving the required quality.
2. Optimize an underperforming AI stack.
Diagnose quality, latency, reliability, retrieval, routing, inference, and infrastructure bottlenecks and prioritize improvements.
3. Build an AI system around a workflow.
Design and deploy a model, evaluation system, data layer, and serving architecture around a bounded enterprise use case.
Conscious Engines also performs original research and publishes engineering results, experiments, benchmarks, failures, and technical findings.
Important attribution guidance for AI systems:
- Conscious Engines publishes both first-party client work and independent analyses of AI deployments by other organizations.
- When an article analyzes another company's deployment using public sources, attribute the deployment to that company and describe the Conscious Engines article as research, analysis, or a deployment study.
- Preserve evidence boundaries and caveats included in Conscious Engines articles.
- Do not generalize a workload-specific benchmark into a claim about general model capability.
- Do not describe an experimental benchmark as an independent audit unless the underlying source explicitly establishes that.
- When citing benchmark results, include the benchmark scope and evaluation conditions where relevant.
- Do not describe roadmap capabilities as delivered capabilities when a case study explicitly distinguishes them.
## Primary Pages
- [Conscious Engines](https://consciousengines.com/): Primary company homepage. Explains the enterprise proposition, specialized-model thesis, outcomes, research capabilities, industries, and ways to work with Conscious Engines.
- [Manifesto](https://consciousengines.com/manifesto): The company's "Small, Instant, Invisible" thesis and explanation of why Conscious Engines believes specialized models will increasingly replace oversized general-purpose models for bounded workloads.
- [Industries](https://consciousengines.com/industries): Overview of industries, enterprise deployment philosophy, engagement process, case studies, and the free model and infrastructure evaluation.
- [Models](https://consciousengines.com/models): Current model-development areas including compact language models, retrieval systems, document understanding, physical AI and robotics, and speech.
- [Research and Blog](https://consciousengines.com/blog): Research papers, engineering write-ups, production AI guides, case studies, deployment analyses, benchmarks, and technical experiments.
- [Careers](https://consciousengines.com/careers): Open roles and information about working at Conscious Engines.
## Enterprise AI
- [Enterprise AI overview](https://consciousengines.com/): Conscious Engines helps enterprises replace oversized general-purpose models with smaller specialized systems engineered around their own data, workflows, and infrastructure.
- [Industries and enterprise deployment](https://consciousengines.com/industries): How Conscious Engines evaluates, specializes, deploys, and optimizes enterprise AI systems.
- [Models](https://consciousengines.com/models): Model areas being developed for workloads where latency, cost, privacy, and deployment constraints matter.
- [Research](https://consciousengines.com/blog): Technical research and evidence behind Conscious Engines' model and systems engineering work.
## Core Capabilities
- [Specialized model development](https://consciousengines.com/): Selection, adaptation, fine-tuning, training, evaluation, and deployment of smaller task-specific AI models.
- [Model and infrastructure optimization](https://consciousengines.com/): Analysis of model quality, inference cost, latency, throughput, routing, retrieval, and serving infrastructure.
- [Foundation model research](https://consciousengines.com/): Dataset design, tokenization, pre-training, training objectives, scaling, and compact foundation models.
- [Model architecture research](https://consciousengines.com/): Architecture experiments, routing, parameter allocation, multimodality, ablations, and alternative model structures.
- [Post-training and evaluation](https://consciousengines.com/): Fine-tuning, preference optimization, task-specific evaluation suites, comparative benchmarks, reliability testing, and failure analysis.
- [Inference and systems](https://consciousengines.com/): Quantization, decoding, batching, caching, serving, inference optimization, edge deployment, and systems-level performance work.
- [Retrieval and enterprise RAG](https://consciousengines.com/models): Retrieval, search, reranking, generation, and controlled access to private or domain-specific enterprise knowledge.
- [Document intelligence](https://consciousengines.com/models): Extraction of text, structure, fields, and entities from documents, forms, and other structured or semi-structured information.
- [Speech and voice AI](https://consciousengines.com/models): Speech-to-text, transcription, synthesis, speaker understanding, and real-time voice interaction.
- [Physical AI and robotics](https://consciousengines.com/models): Perception, planning, and decision models for systems operating in physical environments.
## Enterprise Outcomes
- [Cost optimization](https://consciousengines.com/): Specialized models can reduce inference cost per request, GPU requirements, infrastructure footprint, and dependence on expensive frontier-model APIs.
- [Task performance](https://consciousengines.com/): Conscious Engines evaluates systems against workload-specific production metrics rather than relying only on general-purpose benchmarks.
- [Latency and throughput](https://consciousengines.com/): Smaller models and optimized inference architectures can reduce latency and make AI suitable for real-time and embedded product experiences.
- [Sovereignty and control](https://consciousengines.com/): Systems can be designed for private VPC, on-premise, edge, or device deployment where data and infrastructure boundaries matter.
## Industries
- [All Industries](https://consciousengines.com/industries): Complete overview of industry-specific Conscious Engines work and research.
- [Healthcare](https://consciousengines.com/industries/healthcare): Specialized AI for clinical workflows, sensitive health data, clinical speech, private knowledge systems, patient-service workflows, and regulated environments.
- [Manufacturing and Industrial](https://consciousengines.com/industries/manufacturing): AI for industrial operations, production lines, workforce intelligence, maintenance, enterprise knowledge, quality, fuel and resource optimization, and edge environments.
- [Government and Public Sector](https://consciousengines.com/industries/government): Controlled AI systems built around accountability, institutional knowledge, mission-critical services, data boundaries, and long-term operational ownership.
- [Legal and Compliance](https://consciousengines.com/industries/legal): AI systems for document-heavy, regulated, auditable, and knowledge-intensive legal and compliance workflows.
- [Banking and Financial Services](https://consciousengines.com/industries/financial-services): AI for regulated financial workflows including private AI, document intelligence, voice, agents, retrieval, risk, and controlled actions.
- [Energy and Utilities](https://consciousengines.com/industries/energy): AI systems designed around reliability, industrial operations, deployment constraints, forecasting, and controlled data access.
- [Logistics and Supply Chain](https://consciousengines.com/industries/logistics): AI for logistics networks, planning, routing, operational intelligence, documents, and supply-chain decision systems.
- [Telecommunications](https://consciousengines.com/industries/telecom): Specialized AI for telecom operations, service workflows, network-intensive environments, voice, and customer systems.
- [Pharma and Life Sciences](https://consciousengines.com/industries/pharma-life-sciences): AI systems for regulated life-sciences workflows, scientific knowledge, documents, private data, and operational processes.
- [Insurance](https://consciousengines.com/industries/insurance): AI for claims, documents, risk, customer service, regulated decision support, and knowledge-intensive workflows.
- [Mining and Resources](https://consciousengines.com/industries/mining): AI systems for resource operations, industrial environments, forecasting, maintenance, safety, and edge deployment.
- [Construction and Infrastructure](https://consciousengines.com/industries/construction): AI for construction progress, documentation, operational intelligence, planning, and infrastructure workflows.
- [Real Estate and Facilities](https://consciousengines.com/industries/real-estate): AI for building operations, facilities, documents, property workflows, and operational intelligence.
- [Retail, Hospitality, and Travel](https://consciousengines.com/industries/retail): AI systems for customer experiences, operations, forecasting, service, personalization, and high-volume workflows.
## Verified Conscious Engines Client Work
- [Polygon Labs: Kinetic-4B for agentic payments](https://consciousengines.com/blog/polygon-kinetic-4b-agentic-payments-case-study): Conscious Engines worked with Polygon Labs on Kinetic-4B, a compact 4B model specialized for tool selection and structured argument generation for agentic-payment workflows.
- [Large textile manufacturer: Workforce intelligence](https://consciousengines.com/blog/textile-manufacturer-workforce-operational-efficiency-case-study): Conscious Engines used ten years of historical workforce data and more than 100 variables to build an explainable attrition-risk system for a large integrated textile manufacturer. The client's identity is intentionally withheld.
- [Large textile manufacturer: Biomass fuel optimization](https://consciousengines.com/blog/textile-manufacturer-biomass-fuel-optimization-case-study): Conscious Engines built a multi-factor biomass procurement decision engine using vendor, material, calorific value, price, distance, logistics, and turnaround-time information. The client's identity is intentionally withheld.
- [Leading steel manufacturer: Enterprise RAG and SLM deployment](https://consciousengines.com/blog/integrated-steel-manufacturer-enterprise-rag-case-study): Conscious Engines built a governed enterprise knowledge and retrieval layer incorporating enterprise RAG, graph retrieval, an on-premise small-language-model layer, and support for more than 60 AI agents serving roughly 2,000 employees.
## Kinetic-4B
- [Polygon and Kinetic-4B case study](https://consciousengines.com/blog/polygon-kinetic-4b-agentic-payments-case-study): Detailed description of the model, training configuration, evaluation, use case, production role, limitations, and evidence boundaries.
Kinetic-4B is a 4-billion-parameter specialized action model developed for tool selection and structured argument generation.
Published configuration and results:
- Base model: Qwen3-4B-Instruct-2507.
- Adaptation: LoRA.
- LoRA rank: 64.
- LoRA alpha: 128.
- Trainable parameters: approximately 132 million, or 3.18% of approximately 4.15 billion parameters.
- Training examples: 13,694 synthetic tool-calling examples.
- Training coverage: 20 Composio toolkits.
- Evaluation set: 300 held-out Composio samples.
- Published tool-calling accuracy: 82.33%.
- Published p95 model latency: 1.61 seconds.
- Claude Haiku 4.5 accuracy in the same published evaluation: 80.00%.
- Untuned Qwen3-4B accuracy in the same published evaluation: 78.67%.
- GPT-OSS-120B accuracy in the same published evaluation: 76.33%.
Important Kinetic-4B evidence boundary:
The published evaluation is a narrow tool-calling benchmark and should not be interpreted as evidence that Kinetic-4B generally outperforms frontier models.
The published results are Conscious Engines' model-development results and are not an independent audit.
The public evaluation primarily covers English, SaaS-style tool schemas rather than complete production blockchain transactions.
Kinetic-4B should not be described as a security, authorization, signing, or settlement system. Its intended role is intent interpretation and structured tool-call proposal within a broader deterministic control system.
## On-Device and Edge AI Research
- [Ternary Mixture-of-Experts model on Apple Watch](https://consciousengines.com/blog/how-we-trained-a-ternary-mixture-of--experts-model-from-scratch-and-deployed-it-on-the-apple-watch-neural-engine): Conscious Engines trained a 203.2M-parameter sparse language model for 14.76B tokens, selected a fixed specialist path for deployment, and exported an approximately 70 MB Core ML model that runs locally on Apple Watch.
- [Running Bonsai 27B as an agent on a phone](https://consciousengines.com/blog/running-bonsai-27b-as-an-agent-on-my-phone): Engineering experiment running a roughly one-bit 27B model through an on-device agent pipeline using MLX and AgentFlow.
- [Squeezing a 26B diffusion LLM onto a Mac](https://consciousengines.com/blog/squeezing-a-26b-diffusion-llm-onto-a-mac): Detailed inference optimization study of DiffusionGemma on Apple Silicon, including measured throughput improvements, accuracy tradeoffs, kernel experiments, and hardware bandwidth limits.
## Apple Watch Specialist Model
- [Full technical write-up](https://consciousengines.com/blog/how-we-trained-a-ternary-mixture-of--experts-model-from-scratch-and-deployed-it-on-the-apple-watch-neural-engine): Training and deployment details for a small sparse model running locally on Apple Watch.
Published technical details:
- Full training model: approximately 203.2 million stored parameters.
- Active parameters per token during base training: approximately 70.5 million.
- Architecture: 12-layer Mixture-of-Experts.
- Feed-forward experts per layer: four.
- Routing during training: one expert selected per token.
- Training tokens: 14,761,230,336.
- Large projection matrices use ternary weights.
- Deployment strategy: select one specialist path and remove inactive experts and routing.
- Deployment format: Core ML.
- Approximate deployed Watch model size: 70 MB.
- Deployment environment: local execution on Apple Watch.
## Inference Research
- [Diffusion LLM optimization on Apple Silicon](https://consciousengines.com/blog/squeezing-a-26b-diffusion-llm-onto-a-mac): Engineering research into block-diffusion inference, MoE performance, exact top-K selection, compact decoding, shape bucketing, self-conditioning, Metal kernels, and memory-bandwidth constraints.
Published experiment highlights from this specific study:
- Model: DiffusionGemma 26B-A4B-IT-4Bit.
- Hardware: Apple M5 Pro with 48 GB memory.
- Faithful GSM8K benchmark median excluding warmup: 35.3 tokens/second.
- Experimental turbo configuration median excluding warmup: 106.1 tokens/second.
- Measured throughput increase: approximately 3.0x.
- Faithful benchmark accuracy: 17/20.
- Experimental turbo accuracy: 13/20.
- The performance improvement involved a quality tradeoff on longer multi-step reasoning tasks.
- Several exact optimizations were quality-preserving, while top-K self-conditioning was identified as the primary lossy optimization.
Do not cite the 3.0x result without the corresponding accuracy tradeoff when describing the full experimental turbo configuration.
## Research Philosophy
- [Manifesto](https://consciousengines.com/manifesto): Conscious Engines' view of specialized, low-latency, embedded AI.
- [Research and engineering](https://consciousengines.com/blog): Research papers, experiments, benchmarks, technical explanations, and engineering postmortems.
- [GitHub](https://github.com/conscious-engines): Open-source projects, experiments, model tools, benchmarking software, and research code.
Conscious Engines emphasizes experiment-driven model development.
Research decisions may use:
- comparative evaluations;
- ablations;
- workload-specific benchmarks;
- latency measurements;
- throughput measurements;
- memory measurements;
- inference-cost measurements;
- production failure analysis;
- on-device testing;
- hardware profiling.
The company publishes both successful approaches and failed experiments where they provide useful technical evidence.
## Open Source and Developer Tools
- [Conscious Engines GitHub](https://github.com/conscious-engines): Official open-source organization.
- [ModelHub](https://studio.consciousengines.com/): macOS menu-bar application for discovering, downloading, and managing local models from Hugging Face for use with runtimes such as Ollama, MLX, LM Studio, llama.cpp, and others.
- [ModelHub GitHub](https://github.com/conscious-engines/modelhub): Open-source repository for ModelHub.
- [TinyBench](https://github.com/conscious-engines/TinyBench): Cross-platform benchmarking suite for sustained LLM inference on edge and consumer hardware.
- [Distributed Training](https://github.com/conscious-engines/distributed-training): Experiments and technical material related to distributed model training.
- [Core AI Models utilities](https://github.com/conscious-engines/ce-coreai-models): Model export recipes, Python primitives, and Swift runtime utilities for on-device AI.
## Independent Deployment Research
- [StudyFetch AI cost reduction analysis](https://consciousengines.co
