Introducing SyntheticPulse Beta

Today we're opening SyntheticPulse Beta to our first cohort of teams. This is the product we've been building for 14 months — a platform that lets you generate lifelike synthetic consumers at population scale and run research in hours, not weeks.

1. 14 months of iteration

Eighteen months ago, we asked ourselves a question that felt almost naive: what if you could replace a 500-person focus group with AI agents that behave like real consumers — not chatbots giving politically correct answers, but opinionated, inconsistent, sometimes contradictory synthetic humans that mirror the actual diversity of a target market? At the time, the answer was "probably not yet." The models were too stiff. The architectures for multi-agent simulation were academic toys. And the infrastructure to run hundreds of agents in parallel, each with persistent memory and a coherent worldview, simply did not exist outside of a few well-funded research labs.

Four months of prototyping changed our minds. By late 2024 we had a proof of concept: a swarm of 50 agents arguing about a new beverage concept. The agents generated opinions, changed their minds when presented with counterarguments, and produced aggregate sentiment distributions that looked eerily like real consumer data. We showed the prototype to a dozen market research veterans. Half of them thought we were faking the results. The other half asked when they could buy it.

That was the moment we committed. We spent the next 14 months rebuilding everything — the opinion propagation engine, the vector memory layer, the swarm orchestration runtime, the persona generation pipeline. We ran hundreds of distinct agents through thousands of simulated conversations. We benchmarked against 47 real human panels across five countries. We broke the system, fixed it, broke it again, and iterated until the correlation between synthetic and human responses exceeded 0.89 on standard brand tracking metrics. Today, SyntheticPulse Beta is the result of that work.

2. Why traditional consumer research is broken

The consumer research industry spends an estimated $80 billion annually on methods that have not fundamentally changed since the 1970s. Focus groups cost $8,000 to $15,000 per session and take three to four weeks to recruit, schedule, and execute. Surveys are cheaper but suffer from catastrophic bias — social desirability bias, recency bias, question-order effects, and the fact that most people simply do not have deeply considered opinions about 90 percent of the products they are asked about. When was the last time you had a well-formed view on a toothpaste packaging variant? Exactly.

Then there is the speed problem. By the time a traditional research study returns results, market conditions have shifted. Competitors have launched. Consumer sentiment has evolved. The research that was designed to reduce uncertainty often arrives too late to influence the decisions that mattered most. In the technology sector, where product cycles run in weeks, a four-week research timeline is not just slow — it is disqualifying. Teams make decisions on intuition because they cannot afford to wait for data.

But perhaps the deepest problem is scale. A typical concept test surveys 300 to 600 people. That sample might tell you whether your idea resonates, but it cannot tell you how different demographic segments diverge, how opinions propagate through social networks, or what happens when consumers argue with each other — which is how real opinion formation actually works. Traditional research measures static snapshots, not dynamic belief systems. It gives you a photograph of a landscape that is actually a river.

SyntheticPulse exists because we believe there is a better way — a method that is faster, cheaper, less biased, and capable of modeling consumer opinion as the complex, dynamic, socially-influenced phenomenon it actually is.

3. What SyntheticPulse is

SyntheticPulse is a platform for generating lifelike synthetic consumers at population scale and running structured research studies against them. Think of it as a simulated focus group facility that you can spin up in thirty seconds, populate with any demographic profile you can describe, and run through any research methodology you can design — all without recruiting a single human participant.

Each synthetic consumer in the platform is not a simple prompt template. It has a name, an age, a gender, an income bracket, an education level, a geographic location, a cultural background, a personality profile, consumption habits, brand affinities, and — critically — a belief system about product categories relevant to your research. These beliefs are not hard-coded. They emerge from the agent's persona, its simulated life experience, and the opinions it encounters during swarm interactions. Two agents with identical demographic profiles will disagree on specific questions, just as two real humans with similar backgrounds will. That variance is not noise — it is signal.

The platform supports the full lifecycle of consumer research. You define your target audience. You select or generate a set of synthetic personas. You configure your research methodology — a concept test, a survey, a focus group, a debate, a brand tracker. You launch the swarm and let the agents converse, react, and update their beliefs. And then you extract insights: aggregate sentiment, segment-level breakdowns, verbatim responses, opinion propagation paths, and statistical confidence estimates. Everything that happens in the swarm is auditable. You can replay any conversation, inspect any agent's belief state, and trace exactly how an opinion evolved.

4. How it works — a detailed walkthrough

The workflow consists of four phases. In the first phase, you define your target audience. This is not a simple dropdown. You describe the population you want to model using natural language or structured filters — age range, gender distribution, income percentiles, geographic regions, education levels, household composition, brand usage patterns, category involvement. The platform translates your specification into a statistical sampling plan and generates a persona set that matches your target distribution. If you want 30 percent of your swarm to be Gen Z heavy users of your competitor's product, with household incomes between $50,000 and $80,000 in the Southeast US, the platform will generate exactly that mix.

Phase two is swarm spawning. You select the size — anywhere from 20 to 200 agents in the beta — and the platform instantiates each agent with its persona, its initial belief state, and its position in the opinion propagation graph. The graph is a directed network that determines which agents influence which other agents. By default, it mirrors real-world social influence patterns: some agents are opinion leaders with high out-degree centrality, others are followers who update based on their neighbors, and the structure itself evolves as the simulation runs. The swarm is fully active within seconds.

Phase three is the actual research run. You can deploy any of the built-in research methodologies — concept testing, survey, focus group, ad testing, crisis simulation — or design a custom prompt sequence. The swarm executes asynchronously. Agents see stimuli (product concepts, ad creatives, survey questions, scenarios), form initial reactions, discuss them with their neighbors in the influence graph, potentially update their opinions, and generate final responses. A 200-agent concept test with three rounds of debate completes in roughly 90 seconds. During that time, you can watch the conversation stream in real time, observe sentiment evolve, and intervene if needed by injecting new information or adjusting the stimulus.

Phase four is insight extraction. When the run completes, the platform generates a comprehensive results package: aggregate sentiment with confidence intervals, segment-level breakdowns, top verbatim quotes, opinion polarization scores, and influence maps showing which agents drove consensus and which resisted it. You can export everything as JSON, CSV, or PDF, or pull the data via the REST API into your own analytics stack. Every result includes provenance metadata — you can trace any data point back to the specific agent, conversation, and simulation step that produced it.

5. Key features in Beta

The beta ships with a core set of capabilities designed to cover the most common consumer research use cases. Here is what you get on day one.

Swarm engine (20–200 agents). The heart of the platform. You configure swarm size, agent diversity, influence graph topology, and debate rounds. The engine handles all orchestration — agent lifecycle, message routing, belief updates, convergence detection. Swarms run in a fully deterministic mode for reproducibility or in stochastic mode for creative exploration.

Persona library (15 base personas). Pre-built, validated synthetic consumer profiles spanning major demographic axes — age, gender, income, geography, culture, and category expertise. Each persona has been tested against real human benchmarks for behavioral realism. You can extend any persona with custom attributes or generate entirely new ones through the persona studio.

Real-time sentiment dashboard. A live visualization of swarm sentiment as it evolves during a research run. See aggregate positive/negative/neutral distributions, track sentiment trajectories per segment, and detect opinion polarization events as they happen. The dashboard updates at sub-second latency and supports the full history of every run.

Multi-format export (JSON, CSV, PDF). Every research run produces structured data you can pull into your existing tools. The JSON export includes full agent-level responses, conversation transcripts, sentiment over time, and influence graph snapshots. CSV exports are optimized for import into Tableau, Looker, Excel, or Google Sheets. PDF reports are designed for stakeholder consumption.

REST API. Everything the web UI can do, the API can do. Create personas, spawn swarms, run research, export results — all from your CI/CD pipeline, your internal tooling, or your product analytics stack. The API is authenticated via API keys and supports rate limits of 500 requests per minute in beta.

6. Use cases

Beta testers are already using SyntheticPulse across a range of applications. Here are the four most common patterns we are seeing.

Product concept testing. The flagship use case. You have three product concepts and need to know which one resonates most with your target demographic. Upload your concepts as text or images, specify your target audience, and run a concept test. Results come back in under two minutes with segment-level preference distributions, open-ended feedback verbatims, and a clear winner. Early beta users report that synthetic concept test results correlate with later human validation at r > 0.85 across 12 product categories.

Ad creative A/B testing. You have three ad variants — different headlines, imagery, and calls to action. Upload them to the platform and run an ad test swarm. The agents evaluate each variant on recall, emotional response, purchase intent, and brand fit. Results include heatmaps of which ad elements drove which responses. One beta tester used synthetic ad testing to eliminate two underperforming variants before going to human validation, saving $40,000 in media testing costs.

Crisis simulation. A brand crisis unfolds in real time. How will different consumer segments react to your response? What happens if you apologize? What happens if you stay silent? Crisis simulation lets you model consumer reaction to different communication strategies before you commit publicly. Agents incorporate the crisis stimulus, discuss it through their influence networks, and produce segment-level sentiment projections that help you choose your approach with evidence rather than instinct.

Brand tracking. Continuous monitoring of brand health metrics across synthetic consumer panels. Configure a brand tracker once — set your KPIs (awareness, consideration, preference, NPS), your target demographics, and your tracking cadence — and receive automated reports on brand sentiment trends, competitive position shifts, and emerging perception risks. The synthetic tracker runs in minutes rather than weeks and costs a fraction of a continuous human panel.

7. Architecture — how the simulation works under the hood

At the core of SyntheticPulse is the opinion propagation graph, a directed network that models how beliefs spread through a synthetic population. Each node in the graph is an agent with a belief vector — a high-dimensional embedding representing its stance on the topics relevant to the current research. Edges represent influence pathways, weighted by strength and directionality. When an agent generates an opinion, that opinion propagates through its outgoing edges, weighted by the agent's centrality and persuasiveness. Receiving agents integrate incoming opinions into their own belief vectors using an attention-weighted update function that models how real humans balance new information against prior beliefs.

All agents maintain persistent vector memory — a long-term store of past opinions, conversation history, and learned associations. This is not a chat history buffer. It is a structured memory system implemented as a vector database indexed by semantic similarity. When an agent encounters a new stimulus, it queries its memory for relevant past experiences and uses those retrievals to contextualize its response. This is what gives agents coherent, non-contradictory belief systems across multiple research runs. An agent who expressed a strong preference for sustainable packaging in one study will carry that preference into the next, unless confronted with compelling counterarguments.

The inference layer runs on a distributed LLM orchestration platform that manages prompt templates, context windows, token budgets, and temperature schedules across the swarm. Each agent gets a dynamically constructed prompt that includes its persona profile, current belief vector, relevant memory retrievals, and the incoming stimuli. Prompts are optimized for consistency — we use constrained decoding and structured output formats to ensure that agent responses are parseable, comparable, and grounded in the persona rather than in the model's general knowledge. The orchestration layer handles concurrency, queuing, retry logic, and cost management across potentially hundreds of simultaneous inference calls.

Every simulation is recorded as an immutable event log — agent actions, belief state deltas, influence events, and stimulus exposures are all persisted to an append-only store. This log is the source of truth for all derived analytics, and it means that every insight in the platform is fully auditable. You can rewind any simulation to any point, inspect any agent's internal state, and verify that the reported results are faithful to what actually happened in the swarm.

8. Beta details — how to get access

The beta is free for the first 30 days. No credit card required. We want you to run real research, push the platform to its limits, and tell us what breaks. Every beta participant gets access to the full feature set: swarms up to 200 agents, the complete persona library, real-time dashboards, multi-format export, and the REST API. There are no feature gates, no enterprise-only capabilities held back. The beta is the product.

Here is what is included in the beta: unlimited research runs during the 30-day period; 15 base personas with the ability to customize and extend them; swarms from 20 to 200 agents; all research methodologies (concept testing, ad testing, crisis simulation, brand tracking, custom); real-time sentiment dashboard with full history; multi-format export (JSON, CSV, PDF); full REST API access with 500 req/min rate limit; dedicated Slack channel for support and feedback; weekly office hours with the founding team; and early access to new features before general availability.

To join, visit the beta sign-up page and complete the registration form. We review applications within 24 hours. Once approved, you will receive onboarding materials, API credentials, and a link to your dedicated Slack channel. We are capping the beta cohort at 200 teams to ensure every participant gets direct access to the team, so we recommend applying early.

We are particularly interested in teams who are willing to run side-by-side comparisons between synthetic and human research. If you are a research agency, a brand-side insights team, or a product team running regular concept tests, we want to hear from you. The best feedback comes from people who use the product to do real work and compare the results to what they already know.

9. Roadmap — what comes next

Beta is the beginning, not the end. We have a twelve-month roadmap that extends SyntheticPulse into new capabilities, larger scales, and deeper integrations. Here is what we are building and what you can expect in the coming months.

The adversarial red team capability is one of our most requested features. Imagine stress-testing a product concept not just against friendly consumers but against deliberately skeptical, hard-to-convince synthetic personas designed to find every weakness in your positioning. We plan to ship this in Q3 2026, along with persona libraries for European and Asian markets.

Longitudinal tracking will let you revisit the same synthetic cohort over simulated time — measuring how brand perceptions evolve after repeated ad exposures, how satisfaction changes after a simulated product experience, and how competitive dynamics shift as agents update their beliefs based on market events. This opens up brand health tracking as a continuous, always-on capability rather than a periodic pulse check.

And on the integration front, we are building native connectors for the major BI platforms. If your team lives in Looker or Tableau, you should be able to pull synthetic research data into your existing dashboards alongside your sales data, web analytics, and customer support metrics. The REST API currently supports this, but we want one-click sync that requires zero engineering effort.

10. Join the beta — help us shape the future of consumer research

We are not building SyntheticPulse in a vacuum. The beta program is our way of inviting the sharpest research, product, and strategy teams into the design process. Every piece of feedback you give us — every bug report, every feature request, every "I wish it could also do X" — directly shapes the roadmap. The product you see today is already significantly better than what we had six months ago, and that is because our early design partners pushed us hard.

We want beta participants who are willing to be candid. Tell us when something feels like a toy. Tell us when the correlation with human data does not hold up. Tell us when you would trust a synthetic result enough to make a real product decision — and when you would not. That boundary is the most important thing we need to understand, and we can only understand it with your help.

Here is what we ask in return: use the platform for real research. Run at least three studies during your beta period. Share your results — both the synthetic outputs and, if possible, the human validation data you compare them against. Join our weekly office hours. And tell us what you would need to see to become a paying customer when the beta ends.

The post-survey era does not arrive by itself. It gets built, tested, broken, and rebuilt by the people who use these tools to do work that matters. If that sounds like something you want to be part of, we would love to have you.