Predict how populations may react — before the decision becomes expensive.
Synachron is building a behavioral simulation layer for products, campaigns, policies, and public decisions. It combines reusable synthetic populations with controlled model execution, evidence, and a scientific validation roadmap.
Current stage
Pre-revenue · controlled pilot
The engineering runtime has been verified in a ten-agent provider-backed pilot. Commercial traction and real-world predictive validation have not yet been established.
No revenue, customer, or predictive-accuracy claim is implied by the engineering pilot.
30,000+
synthetic profiles in the current agent library
10 / 10
provider-backed agents completed in the verified pilot
$0.033655
estimated model cost of the verified ten-agent pilot
0
unresolved final failures in that pilot
The problem
Most decisions are tested too late — or not tested at all.
Research is too slow for iteration
Focus groups, surveys, and consulting studies can be valuable, but their cost and lead time make repeated testing difficult.
Teams default to internal opinion
When evidence is unavailable, decisions are driven by hierarchy, instinct, or the loudest person in the room.
Future scenarios are hard to field
Unreleased products, sensitive messages, crisis plans, and policy drafts often cannot be tested openly before commitment.
Existing AI tools lack research controls
Generic chat tools can generate opinions, but they do not provide frozen populations, repeatable execution, cost gates, tenant isolation, or evidence provenance.
The solution
A controlled synthetic population you can query repeatedly.
A Synachron study freezes the scenario, question set, selected agent versions, model configuration, prompt version, and execution limits. The engine runs the simulation, stores hashed outputs and checkpoints, and returns segment-level modeled reactions with explicit provenance.
Frame
Define the decision, alternatives, target population, and what evidence would change the decision.
Freeze
Lock the scenario, questions, agent versions, model, token budget, cost ceiling, and time limit.
Simulate
Execute provider-backed agents with retry recovery, leases, attempt limits, and tenant-safe authorization.
Compare
Review distributions, segments, reasoning summaries, failures, costs, and run-level evidence.
What exists today
The technical core is real. The scientific claim is still being earned.
Verified engineering runtime
Ten real provider-backed agent executions completed with a hard ten-call cap, token and cost reservations, retry recovery, two checkpoints, and a stable result-set hash.
Tenant and evidence controls
Organization-scoped authorization, frozen snapshots, hashed inputs and outputs, lifecycle audit events, and idempotent terminal replay are implemented and verified in controlled conditions.
Scientific validation is next
Synachron has not yet demonstrated population-level predictive accuracy. The next major track is calibration against real polls, studies, experiments, and observed outcomes.
Commercial traction is not yet claimed
The company is pre-revenue and does not currently claim paying customers or validated product-market fit. Initial technical pilots are the foundation for commercial discovery.
Product architecture
One core engine, four commercial entry points.
Business and marketing
Sync Market
Product concepts, positioning, creative, pricing, adoption, churn, and brand-response scenarios.
Government and public sector
Sync Civic
Policy communication, crisis response, stakeholder reaction, program adoption, and implementation sequencing.
Politics and campaigns
Sync Elections
Modeled message response, narrative movement, turnout assumptions, regional scenarios, and coalition dynamics.
Defense & security
AEGIS
Defense and security scenario testing, synthetic expert modeling, critical infrastructure simulation, multi-agent response modeling, and crisis decision support.
Initial market wedge
Start where decisions repeat and the cost of a wrong move is visible.
The initial buyers are not “everyone who makes decisions.” They are teams already paying for research, strategy, creative testing, or public-opinion analysis — and who need more iterations than traditional methods can economically support.
Agencies & consultancies
Use Synachron to screen options, strengthen recommendations, and create a recurring simulation layer inside client work.
Product & growth teams
Compare positioning, pricing, concepts, and customer-risk scenarios before production or media spend.
Research teams
Use synthetic studies as directional pre-research, scenario generation, and a tool for prioritizing what to validate with real respondents.
Public-sector innovation teams
Evaluate communication and implementation scenarios in controlled pilots, without presenting results as representative polling.
Business model
Recurring platform revenue plus controlled usage.
Paid pilots
Founder-led, high-touch projects that validate use cases, build benchmark datasets, and establish willingness to pay.
Subscriptions
Workspace access, population libraries, study management, evidence, collaboration, and recurring usage allowances.
Usage credits
Additional simulations, premium models, larger populations, multimodal scenarios, and high-cost workflows.
Enterprise
Private populations, custom validation, integrations, governance controls, and support agreements.
Go-to-market
Earn trust through narrow pilots before scaling self-service.
Founder-led lighthouse pilots
Select decisions with known outcomes or measurable follow-up so each pilot contributes to validation.
Agency and consultancy channel
Partners can embed Synachron into existing strategy and research retainers, multiplying distribution.
Benchmark-led content
Publish transparent comparisons between simulated and real outcomes, including misses and limitations.
Self-service after repeatability
Expand platform access only after onboarding, guardrails, pricing, and scientific calibration are sufficiently mature.
Defensibility
The moat is not “we use an LLM.”
Agent system
Versioned synthetic agents, genomes, certifications, quality flags, populations, and reusable behavioral profiles.
Execution and evidence layer
Frozen studies, deterministic identifiers, cost and attempt gates, checkpoints, hashes, audit events, and tenant-safe workflows.
Calibration data
Over time, every simulation paired with a real-world outcome can improve validation, segment weighting, and use-case-specific confidence.
Workflow ownership
The long-term product is the place where teams frame, test, compare, approve, and document consequential decisions — not a single model call.
Founder advantage
Built by a team that has already shipped enterprise platforms and driven adoption.
Founder George Pantoulas has worked in marketing, product, and digital platforms since 2001. His previous enterprise workplace product, TRUEteam, was rolled out to approximately 4,500 users across five countries and five languages — experience directly relevant to adoption, enterprise sales, and behavior-driven product design.
Roadmap
From verified runtime to validated decision infrastructure.
Controlled execution core
Tenant-safe runs, frozen snapshots, provider-backed agents, hard cost limits, retry recovery, checkpoints, hashes, and evidence.
Scientific validation framework
Compare simulated results with real polls, surveys, experiments, and historical outcomes; report error by segment and use case.
Commercial pilots
Run paid or design-partner studies with measurable decisions, outcomes, and explicit acceptance criteria.
Repeatable product and distribution
Standardized onboarding, pricing, benchmark reports, partner channel, country expansion, and enterprise integrations.
Risks
The company should be judged by how directly it manages its hardest risks.
Predictive validity
Synthetic responses may not match real behavior. Mitigation: benchmark by use case, publish error, and avoid universal accuracy claims.
Model dependence
Provider behavior, pricing, and availability can change. Mitigation: model abstraction, versioned configurations, multi-provider testing, and frozen provenance.
Trust and misuse
Political or public-sector users may overstate results. Mitigation: product labeling, responsible-use gates, evidence packages, and explicit non-polling language.
Data quality
Weak agent construction creates convincing but unreliable outputs. Mitigation: certification, quality flags, source provenance, calibration, and agent versioning.
Sales cycle
Enterprise and public-sector adoption can be slow. Mitigation: paid pilots, agency channels, and initial focus on faster commercial teams.
The round
€150,000 to move from technical proof to scientific and commercial proof.
The objective is not to scale marketing before validation. The round is intended to complete the validation framework, strengthen the agent and data layer, execute measurable lighthouse pilots, and turn the controlled runtime into a repeatable product.
Current commercial status: pre-revenue. No traction numbers are presented as customer traction until contracts or paid usage exist.
Use of funds
Engineering, validation tooling, observability, and product hardening
Data, agent quality, calibration datasets, and benchmark studies
Founder-led pilots, partnerships, and initial go-to-market
Operations, compliance, infrastructure, and working capital
The opportunity is not to replace research. It is to make disciplined testing available before more decisions.
Synachron has moved beyond a concept: the controlled provider-backed runtime works. The next proof is scientific repeatability and customer willingness to pay.
TALK TO THE FOUNDER