Adaptive
Revolutionize business AI with tailored, private, fast model tuning.. [Contact for Pricing]
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What is Adaptive?
Adaptive ML is a frontier AI startup that developed Adaptive Engine, the world's first Reinforcement Learning Operations (RLOps) platform. The platform enables enterprises to evaluate, fine-tune, and serve specialized large language models (LLMs) using reinforcement learning. If you can measure a business metric, Adaptive Engine can optimize it with RL, allowing companies to outperform frontier APIs with small, specialized models.
Key features include: bootstrap with reinforcement learning using synthetic data; evaluate with bespoke AI judges tailored to business outcomes; guarantee performance with A/B testing before production; and serve & adapt by optimizing with real production feedback. The platform supports multiple RL algorithms (PPO, DPO, GRPO), offers custom Python graders, checkpoint promotion, a new Monitoring tab for training observability, and constrained decoding with JSON Schema support. Adaptive Harmony is their in-house preference tuning stack with inference, training, and RL under a unified codebase.
Adaptive Engine is targeted at B2B enterprise customers who want to deploy LLMs with high customization, privacy, and control rather than using third-party public LLM APIs. Target industries include financial services, healthcare, education, professional services, telcos (like AT&T), and travel. Use cases span enterprise search, business intelligence (text-to-SQL), customer support agents, database query generation, automated customer-service workflows, document RAG, call summarization, and content moderation.
Adaptive pricing
Pricing model: Freemium
Adaptive ML operates on a B2B enterprise SaaS model with no publicly listed free tier. Annual recurring revenue ranges from $120,000 to $360,000 depending on enterprise needs. Custom model fees can reach up to $1 million. The company offers a 'book a demo' approach where enterprises schedule consultations to get custom pricing quotes. All plans presumably include access to Adaptive Engine platform, reinforcement learning tuning, AI judges, A/B testing, monitoring, and the Python SDK. Contact sales for enterprise pricing tailored to specific use cases and scale requirements.
Adaptive pros
- Reduces hallucinations by 25% over GPT-4o (Aïkan case study)
- Outperforms frontier APIs with small, specialized models
- No data collection required - uses synthetic annotations
- Supports multiple RL algorithms: PPO, DPO, GRPO
- Custom Python graders are reusable objects in Adaptive Engine
- Checkpoint promotion gives control over which models reach production
- New Monitoring tab centralizes training telemetry across runs
- Constrained decoding enforces JSON Schema during generation
- Bespoke AI judges predict real production performance before going live
- A/B testing validates real user preference before production deployment
- Production feedback continuously improves specialized agents
- Company knowledge compounds while ownership is retained
- Achieves 51% win rate vs GPT-4o on telco RAG (AT&T case study)
- Gemma 3 4B can outperform models twice its size with RL training
- Python SDK enables easy integration with existing applications
- Self-hosted deployment option for enterprise privacy requirements
- Supports open-source models like Llama, Gemma, and Falcon
- Forward Deployed Engineers embedded to accelerate deployments
Adaptive cons
- B2B enterprise-only model - no consumer/free tier available
- $120,000-$360,000 annual recurring pricing likely prohibitive for startups
- Requires reinforcement learning expertise to use effectively
- Custom model fees can reach up to $1 million
- Primarily focused on open-source models, limited proprietary model support
- Steep learning curve for teams without ML engineering background
- Requires production feedback data to achieve optimal adaptation
- May be overkill for simple prompt engineering use cases
Frequently asked questions about Adaptive
What is Adaptive Engine?
Adaptive Engine is a Reinforcement Learning Operations (RLOps) platform for deploying specialized agents using reinforcement learning and synthetic data. It allows enterprises to fine-tune, serve, and evaluate LLMs for any task. The platform enables companies to bootstrap with reinforcement learning using synthetic data, evaluate with bespoke AI judges, guarantee performance with A/B testing, and continuously optimize with production feedback.
What are the benefits of reinforcement learning?
Reinforcement learning allows models to learn by trying things and receiving scores rather than being shown the right answer. The model learns to produce outputs that score higher. RL needs less data than SFT because it learns from scores, not labeled answers. With RL, companies can surpass frontier performance with small, specialized models without collecting proprietary data, and models continuously improve with every production interaction.
How long does tuning take?
The website indicates that tuning time varies based on the task and model complexity. Style or format changes can work with under 100 examples. Training runs save model state at configurable intervals, and any checkpoint can be promoted to a standalone model for evaluation or deployment. The exact timeline depends on the specific use case, dataset size, and model being tuned. Contact an RL expert for project-specific timelines.
What data do I need to start?
You don't need to bring your own data - Adaptive Engine uses synthetic annotations to significantly reduce production time and costs. A strong model generates training data for a weaker one. You start with real examples, generate variations, and filter for quality. RL needs less data than SFT because it learns from scores rather than labeled answers. For style or format changes, under 100 examples can be sufficient.
What is the difference between SFT and RL?
SFT (Supervised Fine-Tuning) shows the model the right answer, while RL (Reinforcement Learning) lets the model try things and tells it what's better through scoring. RL is more data-efficient because it learns from scores rather than requiring labeled answers. Adaptive Harmony is their in-house preference tuning stack that supports both inference, training, and RL under a unified codebase.
How do you handle security?
Adaptive ML operates on a B2B model targeting enterprises that want privacy and control rather than using third-party public LLM APIs. The platform supports self-hosted deployment options for maximum data privacy. Company knowledge compounds while ownership is retained with Adaptive Engine. The platform is deployed at leading enterprises across sectors including financial services and healthcare where security is critical. Contact sales for detailed security documentation and compliance information.
What is Adaptive Harmony?
Adaptive Harmony is Adaptive ML's in-house preference tuning stack that combines inference, training, and reinforcement learning under a unified codebase. It was built by the architects behind some of the most widely used open-source models. Adaptive Harmony serves as their in-house RL infrastructure, enabling the company to deliver the reinforcement learning capabilities that power Adaptive Engine.
What RL algorithms does Adaptive Engine support?
Adaptive Engine allows users to adjust the optimization algorithm for each RL training run, choosing between PPO (Proximal Policy Optimization), DPO (Direct Preference Optimization), or GRPO. Users can even adjust individual hyperparameters for fine-grained control. For simplicity, Adaptive Engine recommends default settings based on the task, model, and feedback method when users prefer not to configure algorithms manually.
What use cases does Adaptive Engine support?
Adaptive Engine supports enterprise search (best-in-class retrieval accuracy, eliminating hallucinations), business intelligence (specialized AI agents for text-to-SQL and database interfaces), customer support (improved CSAT and reduced escalation rates with autonomous agents), database query generation, automated customer-service workflows, document RAG, call summarization, content moderation, and internal knowledge retrieval. AT&T identified 50+ use cases ranging from text-to-SQL to customer support.
How do I get started with Adaptive Engine?
To get started, install the Adaptive Python SDK with 'pip install adaptive_sdk', then instantiate the Adaptive client with your base_url and api_key. Create a new use case (a workspace for grouping models, logs, and metrics), deploy a model like Llama 3.1 70B, make inference requests, log feedback on completions, and adapt models on previous interaction feedback. For enterprise deployments, book a demo to see how Adaptive Engine bridges the last mile from prototype to autonomous production, or talk with an expert for Forward Deployed Engineer support.