- Speed:Models with a 10 million token context window (e.g. Pokee-Isaac 28B) can process reports, reviews, and sales data in seconds.
- Depth:New meta-type agents like Shepherd allow you to fork, replicate, or revert entire analysis runs, maintaining an editable state.
- Precision:Generative AI, such as the Evo 2 model used to synthesize phages, demonstrates how synthetic data can enrich limited datasets without violating privacy.
1. Foundation models with extended context
Pokee-Isaac 28B is designed to operate within the customer's perimeter, meaning you can use it locally without sending sensitive data to the cloud. Its 10M token window makes it ideal for:- Analyzing entire social media conversations
- Synthesizing multi-channel trends
- Generating executive reports
2. Meta-type agents with persistence
Shepherd, an open-source Python framework, allows you to:- Fork an analysis run to test alternative hypotheses
- Replicate a state to compare approaches
- Revert accidental changes with one click
3. Generative modeling for synthetic data
The Evo 2 model demonstrates how AI can generate new data (e.g. phage sequences) to expand small datasets. You can apply the same principle to:- Create synthetic customer profiles to test new products
- Generate review examples to assess sentiment
- Simulate market scenarios to test strategies
Prompt for review sentiment analysis
Analyze the following set of product reviews and provide:
1. Overall sentiment rating (Positive/Negative/Mixed)
2. Three main themes that emerged
3. Three recommended actions for the company
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Reviews:
- "Great product, fast delivery."
- "The color faded after one day."
- "Does not match the description."Prompt for market trend synthesis
Given a context window containing social media posts, news articles, and sales reports (up to 5k tokens), extract:
- 5 emerging trends with estimated start date
- The reason behind each trend
- Potential market impact (high/medium/low)
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Context:
[paste text here]Prompt for synthetic data generation
Phase 1: Define the goal
Whether you want to map a new audience segment or monitor the magnitude of a crisis, starting with a clear goal reduces noise.Phase 2: Prepare the data
Use Shepherd to create a reproducible work state. Save the raw dataset, variables of interest, and any pre-processing performed.Phase 3: Execute the prompt
Copy one of the templates above. Replace the placeholder with your raw text and send it to your chosen model (local with Pokee-Isaac 28B, cloud with Evo 2, etc.).Phase 4: Validate and iterate
If the results are disappointing, fork a new run with modified prompts. Shepherd allows you to quickly revert if necessary.Practical example: from raw data to executive report
Imagine you want to analyze sentiment on your latest 1,000 tweets.- Upload the tweet CSV to a cloud bucket.
- Use a small Python script to extract text and timestamps.
- Copy the text block into a sentiment analysis prompt (see above).
- Send the prompt to Pokee-Isaac 28B via a local API.
- Shepherd automatically saves the run, so you can compare different sentiment thresholds later.
Result: a 2-page report with sentiment rating, emerging themes, and recommended actions, ready for executive review.
Key takeaways to remember
- Choose the right model:Use Pokee-Isaac 28B for extended context, Evo 2 for synthetic data, and Shepherd for reproducible workflows.
- Write clear prompts:Provide structure, constraints, and expected output to reduce ambiguity.
- Iterate with meta-agents:Forking and reverting allows you to quickly test different variables without losing state.
- Always validate:Compare AI output with secondary sources to ensure reliability.
Conclusion
Modern AI transforms market research from a manual, slow process into a rapid, reproducible insight cycle. By leveraging large foundation models, meta-type agents, and synthetic data, you can achieve analytical depth that was previously unimaginable, all while keeping data within your security perimeter.
Start with a clear goal, adopt the right prompts, and let tools like Pokee-Isaac 28B and Shepherd guide your analysis toward faster, data-driven results.
Next steps
- Set up a local instance of Pokee-Isaac 28B to test large batch processing.
- Install Shepherd and create a first meta-agent for sentiment monitoring.
- Experiment with synthetic data prompts to enrich your research database.