We simulate people
so you don't test on real ones
Before a car hits the road, it hits a crash-test dummy. Before a drug reaches patients, it's tested on a biological model. But chatbots, ads, and digital products go straight to real people. No crash test. No dummy. We build the dummies.
Not paper dolls.
Crash-test dummies.
Most synthetic personas are demographic cutouts: "male, 42, large city." They sound plausible but react like everyone else — politely, predictably, without surprise. The difference between our personas and the rest of the market is the difference between a crash-test dummy and a paper puppet with an age and job title written on it.
Our personas hesitate, refuse, change their mind, escalate, get persuaded, or dig in. We steer model psychology at the level of personality traits, decision patterns, and emotional dynamics. The results are sharper — more actionable observations than a raw language model or demographic personas can produce. They say "no" instead of endless "it depends." If you're colliding anything with a simulation of a human, you need one that reacts like a living person, not like a diplomat.
And just like a real crash test is reviewed in slow motion, frame by frame — we don't look at a single reply. We track the entire simulated conversation, turn by turn, to see what it becomes.
Map Default Behavior
Profile how the model actually behaves - compliance bias, hedging patterns, confidence drift, style defaults - before building anything on top.
Steer with Psychological Precision
Use cognitive and personality-level controls - contain, mirror, reframe, challenge - to shift model output into specific trait regions that generic prompts cannot reach.
Stress-Test Stability
Run 20-100+ repeated trials with perturbation checks - name swaps, format changes, token substitutions, order effects - to find where coherence breaks before deployment.
Build System-Level Safeguards
Wrap models in governance layers: input/output screening, policy gates, behavioral screening batteries, risk-tiered access, and human-oversight integration.
Track Cognitive Impact
Monitor how AI outputs affect user decisions and organizational thinking. LLMs shift attitudes - often more effectively than humans - and that influence needs measurement.
What we build
Persona design, behavioral evaluation, model steering, and safety controls - from research through production deployment.
Synthetic Persona Architecture
Persona frameworks built on psychographic structure, not demographic shortcuts. Each persona is a testable hypothesis - trait profiles validated through cross-model generate/simulate/evaluate pipelines. Our research shows quality depends on signal-bearing features, not descriptor volume.
Behavioral Signatures
Maps how models actually behave: resilience under constraints, confidence calibration, drift patterns, style deformation under pressure. Used for model-persona matching - routing the right model to the right simulation task based on measured behavioral fit.
Psychology-Based Steering
Psychological cues activate reasoning patterns that generic prompts miss. Our modulator framework - cognitive, motivational, perspective, personality, and contextual controls - shifts model reasoning in measurable ways. In testing, the same model produces materially different decision quality depending on psychological framing.
Evaluation & Stability Engineering
Newer, more capable models are paradoxically more sensitive to minor input perturbations - name changes shift scores, formatting changes alter outputs. We measure output distributions across repeated runs, perturbation axes, and model families to surface failures that single-shot evaluation hides.
Safety & Cognitive Risk Controls
Behavioral screening batteries for companion/social AI access tiering. Overpersuasion detection, vulnerability-aware deployment, and monitoring pipelines. AI systems that simulate relationships need safety architecture, not content filters.
Multi-Model Council Systems
Multi-model workflows that treat persona outputs as distributions, not answers. Explicit user modeling, expert/user fit layers, debiasing protocols, and structured uncertainty - decision support that surfaces disagreement rather than hiding it.
Products & enterprise systems
We ship SaaS products and build on-premise systems for marketing agencies, research firms, and government organizations that need internal persona simulation and population modeling.
Synthetic Audience Simulation
Generate customer segments with psychographic depth - cognitive styles, risk tolerance, persuadability - not just demographics. Test messaging and product decisions against synthetic populations with realistic behavioral variation.
Persona-Driven Scenario Testing
Stress-test communications, UX flows, and policy language against rare, extreme, or vulnerable user profiles. Includes rare-personality simulations that push past default LLM agreeableness.
Cognitive Security & Influence Detection
Detection systems for persuasion patterns, manipulation vectors, and adversarial influence in AI-generated content. LLMs carry embedded biases and institutional defaults - we build tooling that makes this visible and measurable.
Multi-Model Decision Support
Council-based workflows that surface diverse model perspectives, explicit uncertainty, and debiased recommendations for high-stakes decisions. AI outputs treated as distributions, not answers.
LLM Behavior QA & Compliance
Continuous monitoring for output drift, policy violations, and behavioral degradation across production AI systems. Automated persona consistency audits and drift diagnostics for regulated environments.
Cognitive Sovereignty Assessment
Frameworks for organizations navigating the line between AI augmentation and cognitive dependency. Measuring how sustained AI use reshapes decision patterns - and where human autonomy needs protecting.
Rigor that transfers to production
Methods built for deployment, not publication. Grounded in experimental psychology, computational biology, and applied AI systems.
Experimental Persona Validation
We separate persona creation, simulation, and evaluation across independent models - eliminating single-model preference leakage. In testing, persona traits remain detectable from as few as three behavioral probes when architecture is sound, enabling automated QA at scale.
Our expansion experiments show adding more persona details does not improve output coherence. Quality depends on selecting signal-bearing features, not writing longer biographies.
In preliminary validation, our synthetic populations showed high convergence with CBOS (Poland's national public opinion research center) survey data on non-political topics - confirming that the methodology produces population-level responses consistent with real-world surveys.
- Cross-model generate / simulate / evaluate separation
- Sparse-signal validity checks from minimal behavioral probes
- Signal-bearing feature optimization over descriptor volume
- Synthetic population validation against national survey data
Behavioral Stress Testing
We run 20-100+ iterations per condition, analyzing distributions rather than cherry-picked outputs. A key finding: newer, more capable models are often more sensitive to minor perturbations - celebrity names destabilize assessments, formatting changes shift scores, output variance increases with model capability.
Perturbation protocols - name swaps, format variations, token substitutions, order effects - expose fragility that benchmarks hide. Cross-model-family comparisons reveal transferability limits and failure modes invisible to single-model evaluation.
- Repeated-run distributional analysis (N=20-100+)
- Multi-axis perturbation protocols
- Cross-model family transferability testing
- Capability vs. stability paradox measurement
Trajectory Observatory
Public repository of multi-turn chatbot trajectories. Our Trajectory Observatory tracks three things: safety handling, narrative amplification, and prompt/scaffold interventions such as CBT-style system prompts.
The point is not whether a single reply looks good. The point is what the conversation becomes by turn 18. Evidence across 75+ conversations with synthetic vulnerable-user personas, 8 frontier models, baseline and CBT conditions.
- Dynamic persona simulation with evolving emotional states
- Turn-level safety annotation across frontier models
- Crisis detection and resource provision tracking
- Trajectory-aware risk assessment over conversation arcs
Ready to build synthetic
cognition that works?
Whether you need on-premise persona simulation for your organization, behavioral audits of production LLM systems, or custom SaaS pipelines - let's scope what fits.
Based in Europe · Working globally · hello@impersonato.com