Teams ready to turn a manual marketing process or AI prototype into a usable product.
03 / TECHNOLOGY · AI API foundation / Agents / Product engineering
Keisaku provides the underlying AI technology that powers usable products: model and API access, prompt engineering, data connections, agent workflows, and the application layer. We design the integration, run representative evaluations, and help teams operate and improve it after launch.
Teams ready to turn a manual marketing process or AI prototype into a usable product.
A scoped application with interfaces, AI/API integration, controls, and measurement.
Task completion, output quality, failures, latency, usage, and API cost.
01 / THE PROBLEM
A marketing process is still spread across spreadsheets, tabs, and manual handoffs.
A promising AI prototype is not connected to the data, channels, or users it needs.
Model output varies too much without prompts, evaluation, and human review.
The team cannot see failures, costs, or where users drop out of the workflow.
02 / PUBLIC EXAMPLE
Volumn.ai says its Lead Finder scanned 305–513 X posts daily during a three-day internal test and surfaced 13–100 qualified leads per day.
OpenTaskAI / Work
Keisaku develops OpenTaskAI and provides the underlying technology that supports the product.
Read case study ↗AI AGENT DEVELOPMENT
Prompts and APIs matter, but users also need clear inputs, review points, reliable fallbacks, and a way to act on the output.
Task-specific instructions, examples, constraints, and evaluation sets.
Interfaces, data connections, workflow state, and user permissions.
Human approval, logs, fallback paths, latency, and cost monitoring.
UNDERLYING AI TECHNOLOGY
We provide the technical layer that lets a team connect model capabilities to its own data, workflows, and product. The architecture is scoped to the use case and the systems already in place.
Expose model capabilities through a clear application interface. Define authentication, request and response formats, timeouts, and error handling.
Select models for each task, manage prompts and structured outputs, and add routing or fallback paths when the workflow needs them.
Connect approved product data or internal knowledge. Control which information a task can access and make relevant context available to the model.
Link research, classification, drafting, and tool calls into bounded steps, with a person reviewing consequential actions.
Test representative inputs, review quality and failures, and define permissions and approval points before wider use.
Observe errors, latency, usage, and API spend; use those signals to refine prompts, models, and the product flow.
03 / THE WORK
The scope is tailored to your product and team. These are the concrete workstreams this service can include.
Define the use case, data flow, API contract, quality bar, and first release.
Provide and connect model API capabilities behind a consistent application interface.
Choose models by task and design routing, retries, timeouts, and fallbacks where needed.
Build versioned instructions, examples, constraints, and output formats for each task.
Connect approved product data and knowledge sources to search and grounded generation.
Coordinate multi-step research, classification, drafting, and action workflows with review points.
Build clear interfaces for inputs, results, approvals, and follow-up.
Test representative cases, check output quality, and add human review and permissions.
Track errors, latency, usage, model/API spend, and completed tasks.
Deploy the workflow and improve it from real usage and failure cases.
04 / DELIVERY
Choose the user workflow and define a useful first release.
Test interface, prompts, API behaviour, and representative tasks.
Integrate product data, AI agents, controls, and delivery surfaces.
Review output quality, usage, failures, speed, and cost before expanding.
05 / MEASUREMENT
We agree on a baseline and the signals that matter before treating activity as progress.
06 / QUESTIONS
Yes. Agent workflows, prompt engineering, model APIs, human review, and evaluation are part of this service.
Both are possible. We start from the current workflow and define a focused scope for the next release.
We test representative tasks, review output quality and failure cases, and include a human decision point where the risk or context requires one.
Yes. We can provide model API capabilities and implement the application-facing integration, including task-specific routing and operational controls.
Yes. We start with your current stack, access rules, and data sources, then design the API and retrieval layer around the workflow.
START A CONVERSATION
Tell us about your product, audience, and the part of this workflow you want to improve.