Pokee-Isaac
Pokee-Isaac is a 28B-parameter language model with a 10M-token context window, optimized for agentic reasoning and multi-turn tool orchestration. It executes function calls, shell commands, and maintains project state across extended reasoning chains without losing recall accuracy at depth. The model deploys in your VPC, on-prem, or on-device hardware, keeping all prompts and intermediate reasoning within your infrastructure boundary. Pricing is usage-based: 0.15 USD per 1M input tokens and 1 USD per 1M output tokens, with no seat licenses or annual commitments. It integrates via OpenAI-compatible API, supporting Python, Node, and curl clients.
Pokee-Isaac is an AI agent, integrating with OpenAI SDK, curl, Python, and Node. InnovaAI scores it 3.6/10 for agency adoption, best for Founder, Project Manager, and Operations roles handling 5+ client meetings per week.
Agency Audit
Pokee-Isaac is a 28B-parameter agentic model with a 10M-token context window, deployable in your VPC, on-prem, or on-device, designed for multi-turn tool orchestration and long-horizon reasoning. AI agent development teams and enterprise consultancies benefit most from adopting it internally, since it eliminates the need to send sensitive codebases, research corpora, or regulated data to third-party cloud APIs. The model excels at function calling and shell command execution, making it ideal for agencies building autonomous workflows that must retain project context across extended runs.
5recommended
60/mo
No paid plan published
Moderate
Illustrative scenario. Not a guarantee. Net capacity needs a verified paid base plan, and none is published for this service, so it is not modeled. Hours saved come from the service estimate; implementation, taxes, and unprovided usage charges are excluded.
- Founder handling agentic code review and refactoring
- Project Manager handling multi-turn research and evidence synthesis
- Operations handling regulated data processing and compliance workflows
- Your team primarily uses single-turn chat or summarization workflows where context window size below 10M tokens is sufficient, since the deployment and infrastructure overhead will not offset the capability gain.
- You lack in-house DevOps or infrastructure expertise to manage VPC, on-prem, or edge deployments, and your vendor stack is locked into managed SaaS endpoints with no self-hosted option.
- Your agency's agentic workloads are experimental or proof-of-concept stage, running fewer than 2-3 times per month, making the fixed operational cost of deployment unjustifiable.
Internal Adoption Path
No paid plan published
60 hr/mo
5 seats × 12 hr each
$4,500/mo
modeled at $75/hr labor rate
No paid plan published
Illustrative scenario. Not a guarantee. No verified paid base plan is published for this service, so subscription cost and net capacity are not modeled. Implementation, taxes, and unprovided usage charges are excluded.
Platform Features
Core capabilities of Pokee-Isaac
10M-token context window
Holds entire codebases, research corpora, and multi-step agent histories in a single active context without fragmenting work across API calls. Allows project managers and strategists to retain full project state across 10+ sequential tool invocations.
Function calling and tool orchestration
Executes multi-turn tool use and shell commands natively, enabling engineers and AI consultants to build agents that plan, test, and iterate without manual intervention between steps.
Private deployment options
Runs in your VPC, on-prem, or on-device without sending prompts to third-party servers. Keeps sensitive legal, finance, and healthcare data inside enterprise-controlled infrastructure for compliance teams and regulated workflows.
OpenAI SDK compatibility
Integrates with existing Python, Node, and curl workflows via OpenAI-compatible endpoint, reducing engineering friction when migrating from other models or adding Pokee-Isaac to existing agent stacks.
Pay-as-you-go usage pricing
Charges per input token (0.15 USD per 1M) and output token (1 USD per 1M) with no long-term commitment, allowing operations teams to scale agent workloads without fixed seat costs or annual contracts.
Frontier agentic benchmarks
Scores 93.3 on RULER at 10M tokens and 70.94 on BFCL v4 function calling, outperforming larger models on long-context recall and tool orchestration tasks that define agent reliability.
What Makes Pokee-Isaac Different
Unique advantages vs similar tools in this niche
10M-token context with real recall
vs GPT-5.6 Luna, Gemini 3.5 Flash Lite, Claude Haiku 4.5Achieves 93.3 RULER at 10M tokens while baselines return 0.0, enabling long-horizon tasks without context fragmentation.
Cost-efficient deployment
vs Cloud GPU clustersRuns on a single RTX 4090 with ~5x KV cache efficiency, reducing infrastructure costs.
Data sovereignty by design
vs Third-party cloud APIsDeploy in your VPC or on-prem, with no prompt data used for training and logs retained only 1 day.
Value Equation
Outcome-likelihood-time-effort assessment for Pokee-Isaac
Limited agency channel
Pokee-Isaac 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 Pokee-IsaacPricing
Pokee-Isaac platform cost to your agency
Pay as you go
- No long-term commitment
- OpenAI-compatible endpoint
- API key access via Developer Console
Enterprise
- Private deployment
- Volume pricing
- Security review
- Integration support
How usage-based pricing works
Pokee-Isaac charges per consumption unit (per 1m input tokens). Below are the component rates the vendor publishes. Each row is a separate charge: your total cost combines them based on your configuration and volume. Component rates range from $0.15 per 1m input tokens.
Final agency cost = (sum of selected component rates) × client usage volume. Confirm a usage estimate with each client before quoting.
Component Rates
Cost per unit: total depends on your configuration and volume
Add-ons
Optional extras priced on top of any main plan
No verified white-label program for Pokee-Isaac: client-facing delivery runs under the platform's native branding.
Market Intelligence
Offer + scale economics for Pokee-Isaac
Limited agency channel
Pokee-Isaac 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 Pokee-IsaacInvestment Decision Framework
Strategic vetting analysis for Pokee-Isaac
Situational Fit
Fit depends on your client mix
Buy If
4Your enterprise consultancy works with regulated data (legal, finance, healthcare) and needs to keep all prompts and reasoning within your own infrastructure boundary rather than sending sensitive context to third-party vendors.
Your AI agent development team builds multi-turn reasoning workflows that require holding large codebases or research corpora in a single context, and you currently fragment work across multiple API calls to avoid token limits.
Your operations or engineering team runs repeated, high-volume agent tasks in production and needs predictable per-token pricing instead of per-request or per-minute billing models.
Your project managers or strategists oversee long-horizon agent projects that require the model to remember multi-step decisions, tool outputs, and project state across 10+ sequential turns without losing recall accuracy.
Skip If
4Your team primarily uses single-turn chat or summarization workflows where context window size below 10M tokens is sufficient, since the deployment and infrastructure overhead will not offset the capability gain.
You lack in-house DevOps or infrastructure expertise to manage VPC, on-prem, or edge deployments, and your vendor stack is locked into managed SaaS endpoints with no self-hosted option.
Your agency's agentic workloads are experimental or proof-of-concept stage, running fewer than 2-3 times per month, making the fixed operational cost of deployment unjustifiable.
Your data sensitivity requirements do not extend to prompts or intermediate reasoning steps, and you are comfortable sending full context to third-party cloud APIs for cost and simplicity.
Bottom Line
Pokee-Isaac is a 28B-parameter agentic model with a 10M-token context window, deployable in your VPC, on-prem, or on-device, designed for multi-turn tool orchestration and long-horizon reasoning. AI agent development teams and enterprise consultancies benefit most from adopting it internally, since it eliminates the need to send sensitive codebases, research corpora, or regulated data to third-party cloud APIs. The model excels at function calling and shell command execution, making it ideal for agencies building autonomous workflows that must retain project context across extended runs.
Reality Check
Pokee-Isaac requires your team to manage deployment infrastructure (cloud GPU, on-prem, or edge hardware) rather than relying on a managed API endpoint. Adoption payoff is strongest for teams running 5+ agentic workflows per month; smaller, ad-hoc agent projects may not justify the operational overhead.
Moderate effort: standard configuration with some customization needed
Academy for Pokee-Isaac
Work through it in order: the course for this service first, then the modules behind it.
Course for this service
Pokee-Isaac Agency Implementation, Building Agentic AI Services
Learn how to architect and deliver agentic AI solutions using Pokee-Isaac's 10M-token context window and multi-turn tool orchestration. This course teaches agencies how to build retainer-based automation services, manage client deployments across VPC and on-prem infrastructure, and structure usage-based pricing models that scale with client complexity.
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.
- Wiring Over WidgetsConcept
The AI agent itself is a commodity, but the value for agencies lies in the integration layer: connecting a pre-built agent to a client's CRM, calendar, and review cycle. This framework shifts focus from selecting the 'best' agent to mastering the wiring process. For example, an agency using Vendasta's white-label AI receptionist for a local business must configure it to match the client's booking rules and follow-up cadence, turning a generic tool into a tailored service. As agentic AI adoption grows (77% of decision-makers now run agents in production), clients expect this customization. Agencies that treat agents as components and invest in repeatable wiring processes can charge retainers for ongoing optimization, rather than one-off setup fees.
- Wiring Over WidgetsConcept
The AI agent market sells finished workers, but the strategic value for agencies lies not in the agent itself, which is increasingly a commodity, but in the wiring that connects it to a specific client's CRM, calendar, and review cycle. This framework, 'Wiring Over Widgets,' argues that agencies that treat agents as components rather than products win. The agent is the widget; the wiring is the integration, customization, and ongoing optimization that turns a generic tool into a tailored solution. For example, a white-label platform like Vendasta provides AI employees, but the agency's role is to configure them for each local business's unique lead flow and follow-up process. This wiring is where retainer pricing originates, as it requires ongoing maintenance and adjustment. Recent research shows that 88% of B2B marketers face foundational gaps, meaning clients need help not just deploying agents, but ensuring their operations can support them. Agencies that master the wiring can charge a premium for the irreducible value they add.
- Integration MoatConcept
The Integration Moat framework holds that the durability of an AI agent engagement is determined by how deeply the agent is wired into a client's existing systems, not by the agent's underlying capability. Since the agent itself is increasingly a commodity, the switching cost for the client lives in the integrations: the CRM fields mapped, the calendar sync, the review-cycle triggers, and the exception-handling rules. Agencies that invest in this wiring create a moat that competitors offering generic agents cannot cross. For example, a white-label platform like Vendasta lets an agency deploy an AI receptionist for a local business, but the real value is in configuring it to the client's booking flow and follow-up cadence. With 77% of AI decision-makers now running agentic AI in production, clients expect this depth, and agencies that deliver it convert one-off projects into retainers.
Decision and risk
How to judge the fit, and the ways it goes wrong.
- AI Agents Rule: Wire the Agent, Not the ProductEvaluation Rule
Treat the AI agent as a commodity component and focus your value on the integration into the client's specific workflows, systems, and review processes.
- AI Agents Rule: Wire the Agent, Not the ProductEvaluation Rule
Treat the AI agent as a commodity component and charge for the integration into the client's specific systems and workflows.
- Commodity Agent vs Wired Component: The AI Agents DecisionDecision Framework
IF your agency can wrap a pre-built AI agent into a client's existing CRM, calendar, and review cycle with minimal configuration, THEN treat the agent as a component and price the wiring as a retainer. IF the agent is deployed as a standalone product without deep integration, THEN you are selling a commodity and will compete on price alone.
- The Productized Agent Trap: Why AI Agent Services Stall Without Client-Specific WiringFailure Pattern
- The Agent-as-Product Trap: Why AI Agent Services Stall Without Client-Specific WiringFailure Pattern
9 modules selected for Pokee-Isaac
Frequently Asked Questions
Answers about pricing, setup, implementation
Pokee-Isaac is a 28B-parameter agentic model that maintains a 10M-token context window for long-horizon reasoning and multi-turn tool orchestration. It executes shell commands, calls functions, and retains project state across extended agent runs without losing recall accuracy. You deploy it in your VPC, on-prem, or on-device to keep sensitive prompts and reasoning within your own infrastructure boundary.
Pokee-Isaac uses custom/enterprise pricing — rates are not published publicly; contact their team for a quote.
AI agent development teams and enterprise consultants building autonomous workflows gain the most value, since the 10M-token context eliminates fragmentation across multiple API calls. Operations and engineering teams running high-volume, repeated agent tasks benefit from predictable per-token pricing. Project managers and strategists overseeing long-horizon agentic projects gain from the model's ability to retain multi-step decisions and tool outputs across extended runs.
Time savings depend on your current workflow. Teams building multi-turn agents that currently fragment work across multiple API calls to stay under token limits typically reclaim 4-8 hours per week by consolidating reasoning into a single 10M-token context. Teams managing regulated data that currently use third-party cloud APIs save 2-4 hours per week on data governance and compliance overhead by deploying privately. Conservative estimate: 1-2 hours per week per agent developer once deployment is stable.
Yes. Pokee-Isaac exposes an OpenAI-compatible endpoint, so it works with existing Python, Node, and curl workflows without code changes. If your team uses LangChain, LlamaIndex, or other agent frameworks that support OpenAI SDK, integration is straightforward. Private deployment requires your team to manage the hosting environment (VPC, on-prem, or edge hardware).
If you deploy Pokee-Isaac on-prem or in your VPC, all prompts and reasoning outputs remain on your infrastructure. Cancellation does not affect data retention or access. If you use the pay-as-you-go cloud endpoint, Pokee-Isaac does not retain prompts or outputs after inference completes, consistent with their data sovereignty commitment.
For teams using the OpenAI-compatible API endpoint, rollout takes 1-2 weeks: engineers integrate the endpoint into existing agent code, run staging tests, and deploy to production. For private deployment (VPC, on-prem, or edge), add 2-4 weeks for infrastructure setup, security review, and DevOps integration. Enterprise customers receive integration support to accelerate this timeline.
Pokee-Isaac is best suited for teams running 5+ agentic workflows per month in production. For experimental or proof-of-concept work, the operational overhead of managing deployment infrastructure may outweigh the benefit. Start with the pay-as-you-go API endpoint to test the model before committing to private deployment.