Ami vs FigureLabs: Features, Pricing & Which Is Better (2026)

A side-by-side comparison of Ami and FigureLabs — features, pricing, and ideal use cases — to help you decide which AI tool fits your workflow.

Ami

AiSDR

Paid

AI GTM agent that picks the audience, writes and launches outbound campaigns, reads the results and fixes what stops working.

Autonomous Campaign Loop: Ami picks the target audience, builds and launches the campaign, reads what comes back and changes what is not working, so each campaign sharpens the next without a human restarting the cycle.
Baked-In GTM Experience: Arrives with 27 industry playbooks and the lessons of 17,150 prior AiSDR campaigns and 19,501 meetings, so a first campaign launches with patterns other teams paid to learn.
Signal-Triggered Outreach: Watches hiring, funding and job-change signals and acts at the moment they happen rather than months later.
Performance Triage: When response rates slip, Ami digs into audience, message and sequence to pinpoint what is breaking and proposes fixes before the budget is spent — flagging, for example, a positive response rate under 1% after 21+ days.
Omnichannel Sequences: Configurable sequences combining email via Gmail or Outlook, LinkedIn connection requests, DMs and InMail, and AI call steps through the Aircall dialer with scripts and automated follow-ups.
Deep Per-Lead Personalization: Researches the top three most relevant data points per lead and personalizes from ICP data, activity, LinkedIn data and HubSpot properties.
Native CRM Sync: Two-way HubSpot sync on every plan and two-way Salesforce sync on higher tiers, with AI research and monitoring running over that CRM data.
Review Mode: Campaigns and Ami's proposed corrections stay drafts until approved, so the agent's autonomy is opt-in rather than assumed.

Founder-Led Outbound: A solo founder builds pipeline without hiring an SDR, starting self-serve with no sales call required.
Rescuing Stalled Campaigns: A revenue team catches a dying sequence early when Ami flags a collapsing positive-response rate and rewrites the audience or message.
Replacing Outbound Agencies: A company that has paid outside firms without results brings the motion in-house under one agent.
Warm-Signal Prospecting: A sales team reaches buyers right after a funding round, a relevant hire or a job change instead of cold-listing an industry.
CRM-Grounded Targeting: A HubSpot or Salesforce team has outreach built from and logged back into existing CRM data rather than a disconnected tool.
Multichannel Follow-Up: A team runs email, LinkedIn and dialer touches in a single sequence with replies handled in 5-10 minutes or in co-pilot mode.

Freemium

AI agent that creates publication-ready scientific figures via text-to-figure, image-to-figure, and vectorization.

Text-to-Figure Generation: Creates complete, composed scientific figures from plain-text descriptions, allowing users to specify panels, annotations, and figure layout that the agent renders automatically.
Image-to-Figure Conversion: Transforms input images (e.g., plots, microscopy snapshots, schematics) into polished figure components suited for publication, preserving scientific detail while improving presentation.
Vectorization and Editable Output: Converts raster graphics into vector representations so figures are editable and scalable for high-resolution publication needs.
Publication-Ready Styling: Applies formatting and styling conventions appropriate for academic journals, producing high-resolution outputs that reduce manual rework before submission.
Rapid Iteration: Generates and refines figures in seconds, enabling fast prototyping and repeated adjustments during manuscript or presentation development.
Precision Preservation: Focuses on preserving underlying data clarity and scientific details while enhancing visual clarity and label legibility for reproducible visuals.
Text-to-figure generation from natural-language prompts
Image-to-figure conversion (convert raster inputs into cleaned, publication-ready figures)
Vectorization of raster graphics to vector formats (SVG)
Fast generation workflow (seconds-scale) for rapid iteration
Outputs optimized for publication (high-resolution and editable vector formats)

Preparing manuscript figures for journal submission: generate composed, publication-ready multi-panel figures from descriptions and source images to accelerate paper submission.
Converting lab outputs into editable graphics: turn raster plots or microscope images into vectorized, editable figures for revision and scaling without quality loss.
Rapid prototyping of visual results: create multiple figure variants quickly to test layouts, annotations, and styles during manuscript drafting or poster design.
Recreating figures from text or notes: produce visual representations of experimental setups, workflows, or conceptual diagrams from written descriptions for methods or review articles.
Improving figure consistency across a manuscript: standardize styling, labels, and panel layouts across multiple figures to meet journal formatting requirements and improve readability.
Create publication figures for manuscripts, posters, and presentations
Convert hand-drawn or raster diagrams into editable vector figures
Rapidly prototype visualizations from experimental descriptions
Produce consistent, publication-ready figure sets with minimal manual redrawing