conw
Conw is a locally-served AI chatbot built on the Conway-Retrain 12B model and served via MLX inference on your own hardware. The system implements a guarded learning loop where team members correct replies, rate them, and explain new vocabulary; unsafe or low-quality responses are filtered automatically before verified examples update the live model. All learning remains private to your account. The model is accessible via an OpenAI-compatible API, allowing Developers to integrate Conw into custom tools and workflows without external API calls or data routing to third-party providers.
conw is an AI chatbot, priced at £15/month on the Pro plan, integrating with OpenAI SDK and Hugging Face. InnovaAI scores it 4.6/10 for agency adoption, best for Developer, Operations Manager, and Project Manager roles handling 5+ client meetings per week.
Agency Audit
Conw is a locally-hosted AI chatbot built on the Conway-Retrain 12B model that learns from your team's corrections and feedback without routing data to external APIs. Developers and operations teams benefit most, since Conw integrates via OpenAI-compatible API for custom workflows and keeps all learning private to your account. The guarded learning loop, where unsafe or low-quality responses are filtered before updating the live model, makes it suitable for agencies building proprietary AI assistants or handling sensitive client work. Adoption pays off if your team runs custom AI integrations or prioritizes data privacy over out-of-the-box convenience.
5recommended
60/mo
$4,480/mo
Moderate
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.
- Developer handling custom AI tool integration
- Operations Manager handling proprietary model training and feedback
- Project Manager handling client-specific vocabulary management
- Your team expects a plug-and-play AI assistant without active feedback loops; Conw's value compounds only if you consistently correct replies and explain new vocabulary.
- Your agency has no in-house Developer capacity to integrate the OpenAI-compatible API or manage locally-served inference on your hardware.
- You need real-time transcription or meeting automation; Conw is a chatbot, not a meeting-capture tool, and does not include audio ingestion or call recording.
Internal Adoption Path
$19.98/mo
$19.98/mo flat plan
60 hr/mo
5 seats × 12 hr each
$4,500/mo
modeled at $75/hr labor rate
$4,480/mo
value − subscription cost
In this model, 5 seats reclaim 60 hours of team time each month. Valued at $75/hr that is $4,500/mo, and after the $19.98/mo subscription it leaves $4,480/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 conw
Guarded learning loop with safety filtering
Team members correct replies and rate them; unsafe or low-quality responses are automatically filtered out before verified examples update the live model. Developers and Operations teams use this to ensure proprietary AI assistants stay accurate and safe without manual review overhead.
OpenAI-compatible API for custom integrations
Developers integrate Conw into internal tools and client workflows without external API calls or data routing to third-party providers. This eliminates the need to choose between convenience and data privacy when building custom AI features.
Private vocabulary memory per account
Team members explain new words and terminology; the model learns agency-specific language without sharing adapter candidates between accounts. Account Executives and Project Managers benefit by getting responses that reflect your client base and industry jargon.
Locally-served inference on your hardware
The Conway-Retrain 12B model runs via MLX on your own infrastructure, keeping all chat history and learning data off third-party servers. Operations teams reduce compliance friction and latency for sensitive client work.
Learning dashboard with pass/failure visibility
Team members see which corrections were promoted to the live model and which were filtered out, making the feedback loop transparent. Project Managers use this to track model improvement over time and identify gaps in training data.
Chat history, pinning, and search
Team members organize and retrieve past conversations without relying on external note-taking tools. Account Executives and Project Managers save time on context-switching by keeping all AI-assisted work in one searchable interface.
What Makes conw Different
Unique advantages vs similar tools in this niche
Transparent learning loop with visible pass/fail states
vs Opaque AI models that don't show how they learnConw shows whether a lesson passed or failed, and keeps useful memory even when weight updates are rejected.
Locally served model for privacy and efficiency
vs Cloud-dependent AI assistants that forward data to external APIsConw runs on a single 16GB iMac, not a GPU cluster, and does not forward questions to an external answer API.
OpenAI-compatible API with pay-as-you-go pricing
vs Proprietary APIs that require custom SDKsDrop-in OpenAI-compatible endpoints allow swapping base_url and going.
Value Equation
Outcome-likelihood-time-effort assessment for conw
Limited agency channel
conw 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 conwPricing
conw platform cost to your agency
Starts at £15/mo (Pro), scales to £30/mo (Max)
Free
- 25K tokens per week
- The full Conway-Retrain 12B model
- Private vocabulary memory that works immediately
- Verified per-user adapter candidates, never shared between accounts
Pro
- 500K tokens per week
- 20× the Free allowance
- The full Conway-Retrain 12B model
- Private vocabulary memory that works immediately
Max
- 2M tokens per week
- 4× Pro · 80× Free allowance
- The full Conway-Retrain 12B model
- Private vocabulary memory that works immediately
No verified white-label program for conw: client-facing delivery runs under the platform's native branding.
Prices as published by the vendor in GBP · your regional price may differ
Market Intelligence
Offer + scale economics for conw
Limited agency channel
conw 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 conwInvestment Decision Framework
Strategic vetting analysis for conw
Situational Fit
Fit depends on your client mix
Buy If
4Your Developers spend 3+ hours per week integrating third-party AI APIs into client tools and want to avoid external data routing by using the OpenAI-compatible API instead.
Your Operations team manages sensitive client data and needs an AI assistant that keeps all learning private to your account rather than feeding corrections into a shared cloud model.
Your Project Managers or Account Executives regularly need custom AI workflows tailored to your agency's vocabulary and terminology, and you're willing to invest time in the learning loop to train the model.
Your team builds AI-powered features for clients and wants to host the inference layer on your own hardware to reduce latency and third-party dependencies.
Skip If
4Your team expects a plug-and-play AI assistant without active feedback loops; Conw's value compounds only if you consistently correct replies and explain new vocabulary.
Your agency has no in-house Developer capacity to integrate the OpenAI-compatible API or manage locally-served inference on your hardware.
You need real-time transcription or meeting automation; Conw is a chatbot, not a meeting-capture tool, and does not include audio ingestion or call recording.
Your budget cannot accommodate the infrastructure cost of running MLX inference on dedicated hardware, or your IT team cannot support a locally-hosted model.
Bottom Line
Conw is a locally-hosted AI chatbot built on the Conway-Retrain 12B model that learns from your team's corrections and feedback without routing data to external APIs. Developers and operations teams benefit most, since Conw integrates via OpenAI-compatible API for custom workflows and keeps all learning private to your account. The guarded learning loop, where unsafe or low-quality responses are filtered before updating the live model, makes it suitable for agencies building proprietary AI assistants or handling sensitive client work. Adoption pays off if your team runs custom AI integrations or prioritizes data privacy over out-of-the-box convenience.
Reality Check
Conw requires your team to actively correct and rate responses to improve the model, which means adoption friction if your workflow doesn't naturally include feedback loops. The locally-served model runs on your own hardware, adding infrastructure responsibility compared to cloud-only alternatives.
Low effort: self-service setup with guided onboarding
Academy for conw
Work through it in order: the course for this service first, then the modules behind it.
Course for this service
Conw Agency Implementation, Private AI Chatbots for Client Workflows
Learn how to deploy Conw's locally-hosted 12B model as a white-label chatbot service for clients. This course covers setting up private vocabulary memory, building the guarded learning loop with your team, integrating via OpenAI-compatible APIs into custom client tools, and packaging these capabilities into retainer-based offerings without external API dependencies.
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.
- Escalation Debt RatioConcept
Escalation Debt Ratio is the share of chatbot conversations that must reach a human before resolution, measured against the share the bot closes alone. Agencies sell the automation number; clients feel the escalation number. A bot that deflects 70% of inquiries but routes the remaining 30% into an unstaffed queue has not reduced client overhead, it has moved it. The ratio matters because retainer renewals track perceived response quality, not ticket volume. Forrester reports 83% of B2C marketing decision makers already work with AI agents, so clients now compare your bot against prior experience rather than against no bot at all. Configure handoff paths before launch: Chatling and FastBots both expose human handover controls, and WotNot pairs its builder with live chat for the same reason. Track the ratio monthly and price ongoing optimization into the retainer, because a bot left unmanaged drifts toward higher escalation as client offerings change.
- Handoff Fidelity ThresholdConcept
Handoff Fidelity Threshold is the point at which an AI chatbot must stop answering and route a conversation to a person. Most agencies sell automation as a coverage number, the share of inquiries a bot resolves without help, but the number that protects the retainer is how cleanly the unresolved share transfers. A bot that resolves 70% of questions and dumps the remaining 30% into a cold queue generates more client complaints than one resolving 55% with a warm handoff that carries transcript, intent, and customer history. Platforms differ here: Chatling and FastBots both ship human handoff as a first-class feature, while Chatbase leans on configurable guardrails and procedures to decide when an agent should stop. Forrester reports 83% of B2C marketing decision makers already work with AI agents, so clients now compare your handoff behavior against tools they have used themselves. Set the threshold per client, document it in the retainer scope, and review transcripts monthly.
- Containment CeilingConcept
The Containment Ceiling is the maximum share of inbound conversations a chatbot can resolve without human escalation before client satisfaction drops. Agencies that measure and price against this ceiling build defensible retainers; those that promise full automation set themselves up for churn. The framework has three inputs: intent coverage (how many question types the bot handles), escalation design (when and how it hands off), and knowledge freshness (how often client data changes). A bot trained on static PDFs may contain 60% of queries in month one but fall to 40% by month three as product details shift. Platforms like Chatling and FastBots expose handoff controls and analytics that make the ceiling visible. Forrester's finding that 83% of B2C marketers now use AI agents means clients expect containment, not perfection. Agencies should report containment rate monthly and treat every escalation as a data point for retainer expansion, not a failure.
Decision and risk
How to judge the fit, and the ways it goes wrong.
- When Chatbot Scope Spans Client-Facing Replies, Gate Autonomy Before You ResellEvaluation Rule
Classify every chatbot workflow by autonomy level before reselling it, and put a human checkpoint on anything that touches client-facing replies or CRM writes.
- Chatbot Resale Rule: Price the Escalation Path, Not the BotEvaluation Rule
Price every chatbot retainer with a funded escalation tier (named human owner, response window, and per-handoff cost) before you quote the automation itself.
- Managed Chatbot Retainer vs One-Off Bot BuildDecision Framework
IF a client treats customer conversations as an ongoing operating expense with recurring question volume, THEN sell a managed chatbot retainer that bundles deployment, knowledge-base upkeep, and monthly optimization. IF the client only wants a launch artifact and will not fund maintenance, THEN scope a fixed-fee build and hand over credentials, because an unmaintained bot degrades into a CX liability within a quarter.
- The Silent Handoff Trap: Why AI Chatbots Stall Agency Retainers in Month 3Failure Pattern
- The Demo-Only Deployment Trap: Why AI Chatbots Fail Agency Retainers After LaunchFailure Pattern
Delivery system
Blueprints and procedures for running it as a service.
- Managed Chatbot Retainer Buildout (10-14 days)Implementation Blueprint
A productized engagement that takes a client from no conversational coverage to a monitored, escalation-aware chatbot running on a white-label platform, then converts it into a monthly optimization retainer. Built for agencies that want recurring revenue instead of one-off build fees.
- Escalation Boundary Mapping (Onboarding)Operating Procedure
- Conversation Log Review Cadence (Retention)Operating Procedure
- Bot Behavior Regression Testing (QA)Operating Procedure
13 modules selected for conw
Frequently Asked Questions
Answers about pricing, setup, implementation
Conw is a locally-served AI chatbot that learns from your team's corrections and feedback. Team members rate replies, explain new vocabulary, and filter unsafe responses; verified examples update the live model after safety checks. The model is accessible via OpenAI-compatible API, allowing Developers to integrate Conw into custom tools without routing data to external providers.
Pro plan is £15 per seat per month and includes 500K tokens per week. Max plan is £30 per seat per month and includes 2M tokens per week. A free tier is available with 25K tokens per week and the full Conway-Retrain 12B model.
Developers benefit most, since they integrate Conw via the OpenAI-compatible API into custom client tools and internal workflows without external API calls. Operations teams gain value by keeping all learning private to your account and running inference on your own hardware. Project Managers and Account Executives improve context retention by using the searchable chat history and learning dashboard to track model improvement over time.
Conservative estimate is 3 to 5 hours per week per Developer seat on API integration and data-privacy workflows, since you eliminate the need to route data through third-party AI services or manage multiple API keys. For Operations and Project Management roles, savings depend on how much time your team currently spends managing external AI tools or documenting client-specific vocabulary; payback is highest if your team spends 4+ hours per week on those tasks.
Conw exposes an OpenAI-compatible API, so Developers can integrate it into any tool or workflow that supports OpenAI SDK or Hugging Face. All learning and chat history remain on your local instance; there are no third-party integrations that route data off your hardware.
Since Conw runs on your own hardware and all learning is private to your account, you retain full control of chat history, corrections, and trained vocabulary. Cancellation does not affect data stored on your local instance.
For non-Developer roles, rollout is low-friction; team members can start using the chat interface immediately. For Developers integrating the API, rollout depends on the complexity of your custom workflows; expect 1 to 2 weeks for a basic integration and longer for multi-tool deployments.
Yes, Conw runs the Conway-Retrain 12B model via MLX inference on your own hardware. Your IT team must provision and maintain the infrastructure; this adds operational responsibility compared to cloud-only AI assistants.