RL Framework
AI Reward Signals & RLHF
Design reward functions and human feedback pipelines that align your AI systems with your business values and customer expectations.
- you own the policy
- open-weight
- LoRA adapters
- simulated first
- Control
- You keep the reins
- You define the objective, the reward, and the constraints. We handle the environment, training, and infrastructure.
- LoRA
- Open-weight policies
- Agents fine-tuned on open-weight models with LoRA, so iteration is fast and the weights are yours.
- Sim-first
- Test before deploy
- Thousands of scenarios run in simulation before anything touches production.
02 / The framework
You bring the decision. We bring the training.
A training framework for builders. You stay in control of what the agent optimizes for; we handle the environment, the loop, and the infrastructure.
You control
- The decision and its constraints
- The reward, and what “good” means
- Your data and how it is evaluated
- When a policy is ready to ship
We handle
- The simulation environment
- The training loop and reward modeling
- Compute and infrastructure
- A trained policy you own and can self-host
03 / The loop
How an agent learns the policy
Define the task and the reward, run episodes in simulation, and ship a policy you own, with a human in the loop on the edge cases.
- TriggerModel outputsCandidate responses to evaluate.
- AI stepScore + rank (RLHF)Outputs are judged for quality and preference.
- IntegrationTraining pipelineSignals feed your fine-tuning runs.
- OutputReward signalA reliable gradient toward the behavior you want.
04 / What it changes
What the build is designed to do
- 01Ensure AI outputs consistently reflect your brand voice and values
- 02Reduce harmful, off-brand, or inappropriate AI responses
- 03Balance multiple business objectives like revenue, satisfaction, and trust
- 04Build customer confidence in your AI-powered interactions
- 05Continuously improve AI behavior through structured feedback loops
05 / Recipes
Where teams point it
A few of the decisions teams hand to a trained agent first.
- 01A local hospitality brand uses RLHF to train its booking chatbot to be warm, helpful, and on-brand, ensuring every digital interaction matches their in-person service standards
- 02A neighborhood restaurant fine-tunes its AI ordering system using staff feedback to handle dietary restrictions, substitutions, and special requests with the same care a human server would provide
- 03A local content platform designs reward signals for its recommendation algorithm that balance engagement with content diversity, preventing filter bubbles in their community
- 04A regional financial advisor uses RLHF to align their client-facing AI assistant with compliance requirements and their conservative, trust-first advisory approach
08 / FAQs
AI Reward Signals & RLHF questions
What is RLHF and why does my business need it?
RLHF stands for Reinforcement Learning from Human Feedback. It is a technique where human evaluators rate AI outputs, and those ratings are used to train the AI to produce better responses over time. Your business needs it whenever you deploy AI that interacts with customers or makes decisions that affect your brand. Without RLHF, AI systems optimize for generic metrics that may not align with your specific values, tone, or business priorities. With RLHF, your AI learns to behave exactly the way your best employees would.
How much human feedback is needed to align an AI system?
The amount varies by complexity, but most business applications achieve strong alignment with 500 to 2,000 rated examples. We design efficient feedback collection workflows that integrate into your team's existing processes, for example, having customer service staff rate chatbot responses during quiet periods, or having managers review AI-generated recommendations weekly. The process is ongoing but becomes lighter over time as the AI internalizes your preferences and generates fewer outputs that need correction.
Can RLHF fix an AI that is already giving bad outputs?
Yes, RLHF is one of the most effective techniques for correcting AI behavior. If your current AI is too aggressive in sales pitches, too formal or too casual in tone, missing important nuances, or generating occasionally inappropriate content, RLHF can systematically correct these issues. We start by identifying the specific behavior patterns that need adjustment, collect targeted feedback on those patterns, and retrain the model. Most behavioral issues can be significantly improved within 2-4 weeks of focused RLHF training.
How do you measure whether AI alignment is working?
We establish quantitative alignment metrics at the start of every engagement. These typically include human evaluation scores on key dimensions like helpfulness, accuracy, tone, and safety, plus automated metrics like customer satisfaction ratings, escalation rates, and complaint frequency. We track these metrics over time to demonstrate improvement and identify areas that need further tuning. Monthly alignment reports show exactly how your AI's behavior is trending relative to your defined standards.
Turn AI Reward Signals & RLHF into something your team actually uses.
Name the work you want this to handle. We will map the build, show what is worth doing first, and what it costs. If there is no fit, we will say so.