Samaya
Samaya Sterling is a post-trained 30B mixture-of-experts model optimized for financial research and investment analysis. It automates end-to-end workflows by accessing SEC filings, earnings transcripts, market databases, and web sources, then orchestrates multi-step research tasks such as company screening, macro analysis, and competitive positioning. The agent generates cited investment reports with granular evidence and achieves 58.9% accuracy on FrontierFinance benchmark tasks at $0.61 USD per query. Agencies can train firm-specific models to encode their research methodologies and historical decisions into the agent's parameters.
Samaya is a research tool, priced at $0.61 a month on the Results plan. InnovaAI rates it 1.6 of 10 for agency adoption, best for Strategist, Account Executive and Project Manager roles.
Agency Audit
Samaya Sterling is a post-trained financial research agent that automates end-to-end investment analysis, company screening, and macro research using finance-specific tools and data sources. It generates cited reports with granular evidence for decision-making. For digital agencies, adoption makes sense only if your team includes strategists or account leads who conduct financial due diligence, competitive research, or market analysis as part of client work. The tool is purpose-built for investment workflows, not general agency operations.
3recommended
30/mo
$2,249/mo
Low
Illustrative scenario. Not a guarantee. Net capacity is the value of reclaimed time at $75/hr, less the lowest verified paid base plan (flat plan cost is shared). Hours saved come from the service estimate; implementation, taxes, and unprovided usage charges are excluded.
- Strategist handling financial due diligence and competitive research
- Account Executive handling SEC filing and earnings transcript analysis
- Project Manager handling market opportunity validation
- Your agency serves non-financial verticals (e.g., e-commerce, SaaS, healthcare, creative services) and does not conduct financial analysis as part of client work. Samaya has no value outside investment and financial research workflows.
- Your team's research needs are qualitative or brand-focused rather than quantitative or financial. Samaya is optimized for SEC filings, market data, and investment theses, not customer sentiment or brand perception analysis.
- Your budget model cannot absorb per-query consumption costs. At $0.61 USD per query, a team running 50+ research queries per month will incur material costs; agencies with tight research budgets should model usage before committing.
Internal Adoption Path
$0.61/mo
$0.61/mo flat plan
30 hr/mo
3 seats × 10 hr each
$2,250/mo
modeled at $75/hr labor rate
$2,249/mo
value − subscription cost
In this model, 3 seats reclaim 30 hours of team time each month. Valued at $75/hr that is $2,250/mo, and after the $0.61/mo subscription it leaves $2,249/mo of capacity for billable client work.
Illustrative scenario. Not a guarantee. Uses the lowest verified paid base plan. Implementation, taxes, and unprovided usage charges are excluded.
Platform Features
Core capabilities of Samaya
End-to-end financial research automation
Conducts multi-step research workflows without manual handoffs, combining company screening, macro analysis, and filing retrieval into a single agent loop. Strategists and account leads compress research cycles from days to hours.
Cited investment research reports
Generates decision-grade reports with granular evidence and source citations from SEC filings, earnings transcripts, market data, and web sources. Eliminates manual citation assembly and reduces fact-checking overhead for research teams.
Finance-specific tool suite
Accesses and analyzes financial datasets, company filings, and market data through tools trained on investment workflows. Outperforms general-purpose AI on screening and macro research tasks compared to frontier models.
Post-trained accuracy on investment tasks
Achieves 58.9% accuracy on FrontierFinance benchmark (investment research workflows) versus 55.9% for Claude Fable 5.1, while operating at 11× lower cost per query. Reduces false positives and incomplete research that require rework.
Firm-specific model training
Encodes your agency's or client's historical research decisions, methodologies, and expert judgment into a custom model. Enables repeatable, branded research workflows that reflect your team's standards and client preferences.
Multi-source data aggregation
Retrieves and synthesizes data from SEC filings, earnings call transcripts, market databases, and web sources in a single query. Removes the need for researchers to manually toggle between platforms and copy-paste findings.
What Makes Samaya Different
Unique advantages vs similar tools in this niche
Post-trained model achieves 58.9% on FrontierFinance, beating Claude Fable 5.1 by 3.0 pp
vs General-purpose frontier models like Claude Fable 5.1Samaya Sterling satisfies 58.9% of rubric items on FrontierFinance, compared to 55.9% for Claude Fable 5.1.
11x lower cost per question at $0.61 vs $6.89 for frontier models
vs Frontier closed modelsA question costs $0.61 with Samaya Sterling, against $6.89 for Fable 5.1, at similar latency.
Co-adapted model and harness with 37 finance-specific tools
vs Generic AI models without specialized financial toolsThe harness provides high-quality financial tools that the model learns to use effectively through training.
Value Equation
Outcome-likelihood-time-effort assessment for Samaya
Limited agency channel
Samaya scored below the agency-resellability threshold (agency_fit_score < 50). The Value Equation projects agency-side outcomes, which don't apply to tools without a clear resell pathway.
Contact SamayaPricing
Samaya platform cost to your agency
Starts at $0.61/mo (Results), scales to $0.61/mo (Conclusion)
Results
Platform capabilities
- End-to-end financial research automation
- Cited investment research reports
- Finance-specific tool suite
- Post-trained accuracy on investment tasks
Conclusion
- Firm-specific models. This training process can also be used to build firm-specific models that encode firm-specific expert judgement via data, tools, and historical decisions in model parameters.
- **Post-training larger models.*
No verified white-label program for Samaya: client-facing delivery runs under the platform's native branding.
Market Intelligence
Offer + scale economics for Samaya
Limited agency channel
Samaya scored below the agency-resellability threshold (agency_fit_score < 50). It's a useful tool but not designed for white-labeled or retainer-based reselling, so we don't publish productized offer economics for it.
Contact SamayaInvestment Decision Framework
Strategic vetting analysis for Samaya
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Weak agency-resell fit
Buy If
4Your strategists spend 6+ hours per week researching client competitors' financial health, market positioning, or investment landscape for pitch decks or strategic recommendations. Samaya automates multi-step research workflows and produces cited evidence in hours instead of days.
Your project managers coordinate research-heavy discovery phases and need to compress the time between research request and actionable insight delivery to clients. Samaya's end-to-end automation reduces handoff cycles.
Your account executives need to validate client business models or market opportunity before scoping engagements, and currently rely on manual SEC filing review or third-party research summaries. Samaya retrieves and analyzes filings, transcripts, and market data directly.
Your team serves financial services, fintech, or investment clients and conducts internal due diligence on those clients' targets or market segments. Samaya's finance-specific tool suite and post-trained accuracy (58.9% on FrontierFinance benchmark) outperforms general-purpose AI on investment-grade research.
Skip If
5Your agency serves non-financial verticals (e.g., e-commerce, SaaS, healthcare, creative services) and does not conduct financial analysis as part of client work. Samaya has no value outside investment and financial research workflows.
Your team's research needs are qualitative or brand-focused rather than quantitative or financial. Samaya is optimized for SEC filings, market data, and investment theses, not customer sentiment or brand perception analysis.
Your budget model cannot absorb per-query consumption costs. At $0.61 USD per query, a team running 50+ research queries per month will incur material costs; agencies with tight research budgets should model usage before committing.
Your existing workflow relies on Bloomberg Terminal, FactSet, or other institutional financial data platforms that your team already licenses. Samaya complements but does not replace those tools, and dual licensing adds cost without clear ROI.
Your team is small (fewer than 3 strategists or analysts) and research is ad-hoc rather than a core repeatable workflow. Adoption overhead and per-query costs will outweigh time savings.
Bottom Line
Samaya Sterling is a post-trained financial research agent that automates end-to-end investment analysis, company screening, and macro research using finance-specific tools and data sources. It generates cited reports with granular evidence for decision-making. For digital agencies, adoption makes sense only if your team includes strategists or account leads who conduct financial due diligence, competitive research, or market analysis as part of client work. The tool is purpose-built for investment workflows, not general agency operations.
Reality Check
Samaya is narrowly specialized for financial research and investment analysis. Agencies without dedicated financial research workflows or those serving non-finance verticals will see no productivity gain. Pricing is per-query consumption ($0.61 USD per query), so cost scales with research volume and team size.
Moderate effort: standard configuration with some customization needed
Academy for Samaya
Work through it in order: the course for this service first, then the modules behind it.
Course for this service
Samaya Agency Implementation, Automating Financial Research Delivery
Learn how to package Samaya's financial research automation into retainer services for investment firms and wealth managers. This course covers setting up firm-specific models, designing research workflows that compress analysis cycles from days to hours, and building recurring revenue through cited report generation and macro screening services.
Open the courseNo Academy modules are published for this service yet. Browse the full Academy
Why this category matters
The commercial case before the tooling.
Core concepts
The mental model you need to price and scope the work.
- Self-Report Decay RateConcept
Self-Report Decay Rate is the framework for estimating how fast a piece of research evidence loses predictive value after collection. Stated preference decays fastest: a survey answer about intent holds for weeks, a behavioral recording holds for quarters, and a structured observation of what people actually did holds longest. Agencies that treat every input as equally durable end up rebuilding strategy decks on stale self-report while the behavioral record underneath has already shifted. The practical move is to date-stamp every insight with its decay class before it enters a client deliverable, then set a refresh interval per class. A conversational capture tool such as Typeform is efficient for stated-preference work, but its output should carry a shorter shelf life than a Hotjar-style session recording of the same funnel. With 69% of marketers publishing more AI-generated content than last year, the volume of cheap self-report keeps rising while its marginal predictive value falls, which makes decay classification a defensible differentiator rather than a research nicety.
- Evidence Depth LadderConcept
The Evidence Depth Ladder ranks research inputs by how much they can be trusted when a client challenges a recommendation. Rung one is self-reported data: survey answers, form responses, stated preferences. Rung two is observed behavior: session recordings, click paths, drop-off points. Rung three is verified outcome: what actually converted, renewed, or paid. Most agency research stacks stall on rung one because forms are cheap and fast, and the resulting decks collapse the moment a client asks why. The framework's rule is that no strategic claim ships on a single rung. Pair a Typeform intake with behavioral evidence before writing the recommendation, and treat tools that only capture stated intent (IdeaStream's Reddit and search-trend signals, for instance) as hypothesis generators rather than proof. The ladder also sets retainer pricing: rung-three evidence commands diagnostic fees, rung-one evidence gets bundled into project scope. Agencies that climb deliberately can defend a recommendation in a renewal meeting instead of re-running the study.
- Self-Report Decay RateConcept
Self-Report Decay Rate is the gap between what a respondent says in a form and what the same person does when observed. Every research tool in this category captures stated data: a Typeform survey, a moderated test, a feedback widget. That data is cheap to collect and fast to report, but its predictive value falls as the decision gets more expensive. The framework asks agencies to score each client decision on two axes, cost of being wrong and distance from the moment of behavior, then route only low-stakes calls to self-report alone. A useful calibration: an Anthropic study gave 50 experts an AI agent whose preferences were set by coin toss, and those experts adopted the agent's views up to 86 percent of the time on judgment calls. If trained specialists defer that readily to an ungrounded opinion, client stakeholders will do the same with a 40-response survey. Pair stated data with observed behavior before it reaches a retainer deliverable.
Decision and risk
How to judge the fit, and the ways it goes wrong.
- When Research Tools Produce Self-Reported Data Only, Add Behavioral Evidence Before ReportingEvaluation Rule
Treat self-reported research as a hypothesis generator, not a verdict, and pair every stated-preference finding with at least one behavioral or observational signal before it reaches a client report.
- When Research Tools Feed Agentic Workflows, Gate the Data Before It ActsEvaluation Rule
Insert a human approval checkpoint between any research tool's output and any automated action that touches client budget, messaging, or third-party platforms.
- Insight Engine vs One-Off Studies: How Agencies Decide Whether to Productize ResearchDecision Framework
IF a client roster shares recurring research questions (positioning, pricing sensitivity, onboarding friction) and the agency can commit to a standing panel plus a fixed instrument library, THEN build a repeatable insight engine that runs the same core instruments across accounts and sells findings as a retainer line. IF each engagement raises a genuinely different question and the agency cannot staff continuous collection, THEN buy single-purpose studies per project and keep the spend inside the brief.
- The Self-Report Trap: Why Research Tools Produce Confident Answers Your Agency Cannot DefendFailure Pattern
- Why Research Tools Produce Confident Answers Nobody Can DefendFailure Pattern
9 modules selected for Samaya
Frequently Asked Questions
Answers about pricing, setup, implementation
Samaya Sterling is a post-trained AI agent specialized in financial research and investment analysis. It automates end-to-end research workflows by accessing SEC filings, earnings transcripts, market data, and web sources, then generates cited investment reports with granular evidence. The tool screens companies, performs macro research, and produces decision-grade answers for investment decisions.
Samaya uses consumption-based pricing. The Results plan costs $0.61 USD per query. The Conclusion plan also costs $0.61 USD per query and includes firm-specific model training and post-training on larger models. There is no free tier.
Strategists and account executives who conduct financial due diligence, competitive research, or market analysis see the most direct productivity gain. Project managers coordinating research-heavy discovery phases compress handoff cycles. Analysts and researchers in financial services verticals automate multi-step workflows. Founders evaluating client business models or market opportunity before scoping engagements reduce validation time.
For a strategist or analyst running 10-15 financial research queries per week, Samaya saves approximately 8-12 hours per month by automating multi-step research workflows, SEC filing review, and report assembly. Actual savings depend on research complexity and current manual process overhead. Agencies with higher research volume see proportionally greater time compression.
Samaya operates as a standalone research agent and does not publish native integrations with common agency tools (Slack, Asana, HubSpot, etc.). Research results can be exported and manually shared with your team. If your agency uses Bloomberg Terminal or FactSet, Samaya complements but does not replace those platforms.
Samaya has low adoption friction. Individual team members can begin running queries within hours of account setup. Firm-specific model training (available on the Conclusion plan) requires 2-4 weeks of historical data collection and training, but this is optional and does not block initial adoption.
Samaya does not publish a data retention or export policy in available documentation. Before committing, confirm with their sales team whether research reports, queries, and firm-specific model parameters can be exported or deleted upon cancellation.
No. Samaya is a research automation agent, not a financial data terminal. It accesses market data and filings as inputs to research workflows, but does not provide real-time pricing, portfolio analytics, or the full institutional feature set of Bloomberg or FactSet. Use Samaya to compress research time; keep your existing data platform for live market data and portfolio management.