Teammately is live · pricing from $2/TC · last verified 25 Oct 2026 · viability monitor
Teammately
Teammately is AI alignment infrastructure that turns your experts' judgment into rubrics, coverage, and tested agent harnesses.
If nobody has written down what "good" means for your AI, Teammately is pointed directly at that gap: named agents cover elicitation, coverage, benchmark eval, and harness improvement in one loop, with every rubric criterion traceable to expert evidence. Lemon and Elicit are included in the platform subscription, but Lemon Weave, Code, Goal, and Trialground draw on a shared metered Credit pool at $2.00/TC (Enterprise) or $3.00/TC (Enterprise Critical) as proposed September 2026 rates, plus customer-funded target-model inference. Great fit for Fortune 500-scale teams with budget for a scoped deployment; overkill for simple eval.
Verdict generatedVerified 5d ago · liveness 60/100 · cite: rightaichoice.com/tools/teammately
- Enterprise AI teams whose domain experts hold the judgment criteria but have never written them down
- Retail and merchandising programs where relevance alone doesn't make a recommendation correct
- Organizations that need benchmarks covering rare, consequential conditions with explicit proportions
- Teams running agent harnesses that need trajectory-level evaluation against expert rubrics
- Hobbyists or beginners experimenting with AI who don't need alignment infrastructure
- Simple chatbot use cases where a handful of golden examples settles evaluation
- Teams that already have authoritative, written-down criteria and just want tracing and dashboards
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Skip Teammately if your evaluation criteria are already written down and authoritative — you'd be paying for elicitation, coverage, and harness-improvement machinery you don't need.
Lemon Weave, Code, Goal, and Trialground all draw on one shared Credit pool, so heavy synthesis or repeated benchmark runs consume the same balance.
Teammately fits funded enterprise AI programs with a scoped deployment budget: a platform subscription plus metered Credits at $2.00/TC (Enterprise) or $3.00/TC (Enterprise Critical) per the proposed September 2026 rates, plus customer-funded inference. Cheaper than standing up a dedicated alignment org; more expensive than flat-rate eval dashboards aimed at single teams.
In short
Teammately — Teammately is AI alignment infrastructure that turns your experts' judgment into rubrics, coverage, and tested agent harnesses. Best for Enterprise AI teams whose domain experts hold the judgment criteria but have never written them down, Retail and merchandising programs where relevance alone doesn't make a recommendation correct, Organizations that need benchmarks covering rare, consequential conditions with explicit proportions. Plans from $2/TC.
What is Teammately?
Teammately — Teammately is AI alignment infrastructure that turns your experts' judgment into rubrics, coverage, and tested agent harnesses. Best for Enterprise AI teams whose domain experts hold the judgment criteria but have never written them down, Retail and merchandising programs where relevance alone doesn't make a recommendation correct, Organizations that need benchmarks covering rare, consequential conditions with explicit proportions. Plans from $2/TC.
Teammately is AI alignment infrastructure for enterprises whose AI needs to reflect domain expertise that has never been written down. Instead of collecting a dataset once and hoping it generalizes, it runs a continuing loop: AI agents elicit expert judgment, convert it into policies and rubrics, make coverage across ontologies explicit, then use those same standards to improve harnesses and weights. Work is split across named agents. Lemon is the alignment agent; Lemon Elicit prepares and adapts reviews, comparisons, chat sessions, and voice interviews to surface the reasoning behind a judgment. Lemon Weave constructs cases, worlds, and training material using Bower, a coverage-directed synthesis model, across text, image, document, workbook, presentation, codebase, and mixed-artifact cases. Trialground runs agent harnesses in disposable sandbox computers and evaluates outputs and recorded trajectories against expert rubrics, with judges including Kestel, a specialized binary rubric model. Lemon Goal investigates benchmark failures, coordinates subagents, and tests competing harness prototypes. Rubrics express benchmark criteria while policies organize scope and precedence across teams, and every criterion traces back to its expert evidence — conflicts between experts get investigated rather than averaged away. Coverage engineering compares imported production interactions against planned representation, keeping rare and consequential conditions explicit. Published September 2026 rates put Teammately Credits at $2.00/TC on Enterprise and $3.00/TC on Enterprise Critical, on top of a platform subscription scoped to your deployment; target-model inference is customer-funded. Built for enterprise AI teams where a relevant answer can still be the wrong one — retail merchandising, enterprise software, regulated industries — and where business context lives in people's heads rather than a spec document.
Key features
- AI-led expert elicitation via reviews, comparisons, chat, and voice interviews
- Lemon Elicit prepares and adapts expert sessions and consolidates review learning
- Policy and rubric drafting with conflict detection and proposed clarification
- Every rubric criterion traceable to its expert evidence
- Coverage across ontologies with explicit situations and proportions
- Compares imported production interactions against planned benchmark representation
- Lemon Weave case synthesis across text, image, document, workbook, presentation, codebase, and mixed artifacts
- Bower, a coverage-directed synthesis model, for case generation
- Lemon Weave world synthesis — content-only and executable sandbox environments
- Trialground runs agent harnesses in disposable sandbox computers
- Trajectory and output evaluation against expert rubrics
- Kestel, a specialized binary rubric model, among supported judges
- Lemon Goal investigates benchmark failures and tests competing harness prototypes
- Rubric-guided training data augmentation for demonstrations, preference pairs, and optimization
- Included Lemon and Elicit continue within contracted scope when paid Credits run out
Behind the verdict
Teammately sells alignment infrastructure rather than an eval dashboard, and the distinction shows in the product surface. The platform is organized around five capabilities the vendor lays out explicitly — Elicitation, Rubrics, Coverage, Benchmark Eval, and Improve — and each has a named agent behind it. Lemon Elicit runs the expert-facing side, preparing and adapting reviews, comparisons, chat sessions, and voice interviews so an expert contributes reasoning rather than a one-off label. Lemon Weave handles synthesis, constructing cases, worlds, and training material across text, image, document, workbook, presentation, codebase, and mixed-artifact profiles, using Bower as its coverage-directed synthesis model. Trialground runs harnesses in disposable sandbox computers and scores both outputs and recorded trajectories against expert rubrics, with Kestel available as a specialized binary rubric judge. Lemon Goal closes the loop by investigating benchmark failures, coordinating subagents, and testing competing harness prototypes. The mechanism that separates Teammately from rubric-based evaluation dashboards is traceability. Every rubric criterion ties back to the expert evidence that produced it, and when two experts conflict, Lemon investigates and proposes focused clarification rather than averaging the disagreement away. Coverage engineering then compares imported production interactions against planned benchmark representation, so rare and consequential conditions stay visible with explicit proportions instead of being swamped by common cases. The cost structure is real and multi-part. A platform subscription is scoped to your deployment and program; Lemon and Elicit are included in it. Lemon Weave, Code, Goal, and Trialground draw on a shared pool of Teammately Credits, priced at $2.00/TC on Enterprise and $3.00/TC on Enterprise Critical in the proposed September 2026 rates. Target-model inference is customer-funded or separately authorized, and dedicated infrastructure or custom implementation is quoted separately. Those rates take effect only when incorporated into a signed Order Form, so what you actually pay depends on the contract you sign. The honest caveat is that this is a program, not a purchase. It depends on ongoing expert participation in elicitation and review rather than one-time setup, and if your criteria are already written down and authoritative, you are paying for machinery you do not need. Enterprise Critical adds restricted AI processing, controlled network access, and controlled operational access with configuration confirmed before activation — that tier exists for regulated environments, and it costs $3.00/TC versus $2.00/TC on Enterprise for the same work.
Models under the hood
as of 2026-10-25
Limitations
- Pricing is metered and multi-part: a platform subscription scoped to the deployment, a shared Teammately Credit pool used by Lemon Weave, Code, Goal, and Trialground, plus external target-model inference that is customer-funded or separately authorized.
- Rates are proposed (September 2026) and take effect only when incorporated into a signed Order Form, and availability depends on the contracted configuration.
- The program depends on ongoing expert participation in elicitation and review rather than one-time setup, and it is enterprise alignment infrastructure aimed at production programs.
as of 2026-10-15
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Is Teammately still active?
Teammately is live · pricing from $2/TC · last verified 25 Oct 2026 · viability monitor.
Viability score
How well maintained and how widely used is Teammately? Built from what the vendor actually publishes (docs, changelog, tutorials, integrations, pricing), whether the site is live, and how much real users discuss it. How we calculate this
Last calculated: October 2026
How we score →What's new in Teammately
Checked 7 days agoAcross the latest 1 update: 1 pricing change.
What do users say about Teammately?
How we verified this
Every listed fact about Teammately— pricing, features, links, status — is re-read from the vendor's own pages by our verification pipeline and each change is logged with a date; the last pass ran on 25 Oct 2026. Pages mix model-assisted synthesis with human review; see how we verify.
Verification history
We have re-verified Teammately 10 times since . Each pass re-reads the vendor's own pages and re-checks every listed field against that evidence; passes where nothing had changed are marked as such.
- — changed pricing page
- — changed SSL certificate
- — changed pricing fit, description, who should skip it, setup time, our verdict, hidden costs, switching in, switching out, models
- — changed summary, pricing tiers, who should skip it, our verdict, description
- — changed pricing model
- — changed pricing page, models, limitations
Showing the 6 most recent of 10 verification passes.
Free to cite with attribution — this page re-verifies continuously.
How much does Teammately cost in 2026?
Teammately: Plans from $2/TC. Pricing last verified 25 Oct 2026.
Plans compared
Every published Teammately tier, its price as the vendor publishes it, who it fits, and what it adds over the tier below. The 12-month cost projection follows.
| Plan | Price | Billing | Who it's for |
|---|---|---|---|
| Enterprise | $2/TCVendor: “$2.00/TC USD per Teammately Credit” | usage | Enterprise AI teams running a scoped alignment program where experts hold unwritten judgment and the deployment has standard data-processing requirements.Adds: Starting tier: complete platform with AI-led elicitation, rubrics, coverage, synthesis, managed evaluation, and optimization, with Lemon and Elicit included in the platform subscription. |
| Enterprise Critical | $3/TCVendor: “$3.00/TC USD per Teammately Credit” | usage | Regulated or security-constrained AI programs with mandatory restrictions on data processing, network access, and operational access.Adds: Adds restricted AI processing with approved providers and locations, controlled network access to approved destinations, and controlled operational access with agreed approval and logging — at $3.00/TC versus $2.00/TC. |
12-month cost
Project the real annual outlay, including the implied monthly cost when only an annual tier is published.
Vendor list price only. Add-on usage, seat overages, and contract minimums are surfaced under Hidden costs & gotchas.
Where the pricing makes sense
The company stage and team size where Teammately's pricing actually pencils out — and where peers do it cheaper.
Teammately fits funded enterprise AI programs with a scoped deployment budget: a platform subscription plus metered Credits at $2.00/TC (Enterprise) or $3.00/TC (Enterprise Critical) per the proposed September 2026 rates, plus customer-funded inference. Cheaper than standing up a dedicated alignment org; more expensive than flat-rate eval dashboards aimed at single teams.
Has Teammately's pricing changed?
No plan price for Teammately has moved since we began logging in Aug 2026; the pricing page is re-read on every verification pass.
Who should pick Teammately?
Pick Teammately if you are Enterprise AI teams whose domain experts hold the judgment criteria but have never written them down, Retail and merchandising programs where relevance alone doesn't make a recommendation correct, Organizations that need benchmarks covering rare, consequential conditions with explicit proportions, Teams running agent harnesses that need trajectory-level evaluation against expert rubrics.
Look elsewhere if you are Hobbyists or beginners experimenting with AI who don't need alignment infrastructure, Simple chatbot use cases where a handful of golden examples settles evaluation, Teams that already have authoritative, written-down criteria and just want tracing and dashboards, Buyers looking for a self-serve tool with flat monthly pricing and no deployment scoping.
Skip it if Skip Teammately if your evaluation criteria are already written down and authoritative — you'd be paying for elicitation, coverage, and harness-improvement machinery you don't need.
GeneratedReal-world workflow fit
Concrete scenarios for the personas Teammately actually fits — and what changes day-one when you adopt it.
A senior merchandiser knows when a recommendation is correct but has never written the criteria down. Lemon Elicit runs comparison and review sessions to surface the reasoning, drafts policies and rubrics for the expert to review, and flags where two experts disagree so the conflict gets investigated instead of averaged.
Outcome: The merchandiser's judgment becomes a rubric set where every criterion traces back to the evidence that produced it, ready to measure the model against.
Existing benchmark cases only cover common conditions. Coverage engineering compares imported production interactions against planned representation, then Lemon Weave synthesizes the missing text, workbook, and codebase cases — including executable worlds for harness testing.
Outcome: Rare and consequential conditions appear in the benchmark with explicit proportions, and Trialground scores recorded trajectories against the expert rubrics in disposable sandbox computers.
A benchmark failure could come from the harness or the weights. Lemon Goal investigates the failure, coordinates subagents, and tests competing harness prototypes while tracking protected behavior, with each judgment connected to its criteria and evidence.
Outcome: Engineers get tested harness ideas and rubric-guided training data for demonstrations and preference pairs, with gains and regressions visible against planned coverage.
Use cases
- Turn a senior merchandiser's tacit judgment about what makes a recommendation correct into rubrics your model can be measured against.
- Build benchmark coverage that explicitly represents rare, consequential conditions instead of only the common cases.
- Synthesize text, workbook, presentation, and codebase cases via Lemon Weave to stress-test an agent harness.
- Run an agent harness in a disposable sandbox computer and score recorded trajectories against expert rubrics.
- Investigate which benchmark failures come from the harness versus the weights using Lemon Goal.
- Augment training data with rubric-guided demonstrations and preference pairs from the same expert distinctions.
- Compare competing harness prototypes under a single approved policy and rubric set.
- Trace a benchmark result back through its rubric criterion to the expert contribution that produced it.
Setup time & first value
How long it actually takes to get something useful out of Teammately — broken out by persona, not the marketing-page minute.
Expect days, not minutes: the platform arrives scoped to your deployment and program, so configuration, project context, and expert onboarding come first. Lemon and Elicit are included and ready once the workspace is set up, so the first elicitation session can start as soon as your experts are scheduled — but the first credible rubric set depends on their availability.
Switching to or from Teammately
How to bring data in from common predecessors and how to get it back out — written for the switcher, not the buyer.
- →From a spreadsheet of golden examples: import them as seed cases, then use Lemon Elicit to surface the reasoning behind each judgment and turn it into traceable rubric criteria.
- →From a hosted eval dashboard: re-express your existing criteria as policies and rubrics, then run Trialground against them to establish a trajectory-level baseline.
- →From ad-hoc expert review sessions: route them through Lemon Elicit so preparation, review learning, and checkpoint consolidation happen in one place.
- →From production interaction logs: import them for coverage comparison so emerging intents and missing combinations inform coverage updates.
- ↗To an in-house alignment function: export rubrics, policies, and their expert evidence from Correctness Governance and stand up the loop internally.
- ↗To a lighter eval dashboard: extract your rubrics and benchmark cases from Benchmark Datasets and re-register them in the new tool.
- ↗To a training-only pipeline: take the rubric-guided demonstrations and preference pairs produced by Lemon Weave into your existing fine-tuning stack.
Integrations
Tutorials & Learning
YouTube returned 6 videos for “Teammately”, and we withheld 6: 6 could not be judged, because “Teammately” is a single word that other videos use for other things. We are showing none, because we could not prove any of them are about Teammately.
Official links
What are the best Teammately alternatives?
The closest alternatives to Teammately we list are Spool, Future AGI and LangSmith. Ranked by direct product-type match, not category overlap.
| Alternative | Pricing | Starts at | Last verified |
|---|---|---|---|
| Spool | Free | Free | — |
| Future AGI | Freemium | Pricing not published | — |
| LangSmith | Freemium | Pricing not published | — |
Alternatives to Teammately
View all
Spool
Spool turns your team's AI agent sessions into a shared, searchable library you can read, publish, and resume.
Future AGI
Agent evaluation platform that simulates voice and chat agents, traces production, and scores hallucinations before your customers hit them
Tools that pair well with Teammately
Common stack mates teams adopt alongside Teammately, with the specific reason each pairing earns its keep.
Spool
Spool turns your team's AI agent sessions into a shared, searchable library you can read, publish, and resume.
Future AGI
Agent evaluation platform that simulates voice and chat agents, traces production, and scores hallucinations before your customers hit them
LangSmith
LangSmith is agent and LLM observability from the LangChain team: trace, monitor, evaluate, and deploy AI agents.
Teammately vs its closest rival
Teammately's closest head-to-head is with Spider Cloud — Teammately is the right choice if you need an autonomous AI engineering platform for rigorous prompt evaluation, iteration, and observability in production. Spider Cloud wins if your priority is fast, low-cost web data extraction for RAG or AI agents. They are complementary: Spider Cloud supplies real-time data; Teammately refines and monitors AI services. Verdict generated
Head-to-head comparisons
Teammately vs Spider Cloud
Teammately is the right choice if you need an autonomous AI engineering platform for rigorous prompt evaluation, iteration, and observability in production. Spider Cloud wins if your priority is fast, low-cost web data extraction for RAG or AI agents. They are complementary: Spider Cloud supplies real-time data; Teammately refines and monitors AI services.
GeneratedTeammately vs Temporal Ai
Temporal AI is for teams building mission-critical, fault-tolerant AI agents and workflows that must survive failures, while Teammately is for AI engineers automating the evaluation and iteration loop. Choose Temporal if you need durable execution and state recovery; choose Teammately if your priority is rigorous evaluation and prompt refinement.
GeneratedTeammately vs Presto Voice
Presto Voice is purpose-built for QSR chains needing proven drive-thru AI with measurable revenue lift, while Teammately targets AI engineering teams automating evaluation and iteration for production systems. Choose Presto for immediate drive-thru ROI; choose Teammately for building reliable, self-improving AI services.
GeneratedFrequently asked questions
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