sdpwr / AI development
Build knowledge assistants, intelligent applications and agents around a defined business task. Connect approved data, evaluate performance and keep human review where decisions need it.
Knowledge assistants, content workspaces, support copilots and task agents with a bounded set of business actions.
A working application, approved source connections, review controls, an evaluation set and operating guidance.
The agreed system supports a defined task. Sensitive decisions and external actions remain subject to named approval rules.
The starting point is a useful business output, supported by suitable data and a clear review path.
Build an assistant that answers from approved product, policy or operating documents and shows supporting sources. Restrict retrieval by user permissions and route unanswered or ambiguous questions to an appropriate person.
Create a workspace for product drafts, multilingual content or structured document analysis. Preserve source material, editing history and reviewer decisions so an approved output can be distinguished from a generated draft.
Develop agents that retrieve records, prepare proposed changes or complete a limited set of approved tasks. Define permitted actions, spending limits and approval gates before enabling any write to a business system.
Useful AI software needs source management, understandable behavior and an operating cost that can be reviewed.
Inventory permitted sources, assign content owners and define update and deletion rules. Agree which information may reach an external model service and assess its retention settings before using business data.
Deliver task instructions, output formats and a review interface appropriate to the application. Record the relevant configuration and reviewer outcome so changes can be evaluated and an earlier configuration restored if needed.
Compare suitable services against the agreed quality, response-time and cost requirements. Set usage visibility and operating limits, and document any external service charges or availability dependencies separately from development fees.
Evaluation uses agreed examples and explicit failure conditions. A convincing demonstration alone is not acceptance evidence.
Build a reviewed set of ordinary, difficult and unsupported requests. Agree measures such as source accuracy, required-field completion and review effort, then record results against the baseline and acceptance thresholds.
Test cross-role access, instructions embedded in documents and requests for unauthorized actions. Apply permission checks outside model-generated text and require human approval for agreed sensitive changes or publication.
Define what happens when sources are missing, outputs fail validation or a model service is unavailable. The system should stop the affected action, preserve useful context and give a reviewer a practical way to continue.
Agree on the business scope, then build, validate, and improve in stages.
Choose a target user, a bounded output and the decisions that need human judgment. Establish a baseline for the current work.
Confirm data permissions, curate representative examples and agree quality thresholds, operating limits and approval requirements.
Implement the application and review controls. Compare outputs, costs and failure behavior against the agreed evaluation set.
Run a limited pilot, review real exceptions and confirm operating ownership. Expand access only after the agreed acceptance review.
Not necessarily. An existing model with approved knowledge sources may be sufficient. We assess retrieval, configuration and any need for further training against the task, available data, evaluation results and budget.
No system can promise error-free answers. We define supported topics, require source evidence where appropriate and build a route for uncertainty or unsupported requests. Human review remains necessary for decisions with material business consequences.
That depends on the agreed architecture. Data destinations, permitted fields, retention settings and access controls are reviewed before use. Hosting and model options must fit your information-handling requirements and the selected provider's terms.
AI development delivers a system that can answer, generate, analyze or propose actions. AI automation defines when work starts, how records move between systems and how approvals, retries and exceptions are handled during ongoing operation.
We use agreed evaluation cases and thresholds, permission tests and a supervised pilot. Readiness is specific to the approved task and operating conditions; it does not imply that the system is suitable for unrestricted use.
Connect a specific business problem with the right engineering, intelligence, and growth capabilities.
Plan and build commerce websites, business workspaces and connected software with defined permissions, testable workflows and an agreed handover.
Connect commerce operations through controlled AI workflows with defined triggers, approvals, duplicate protection, exception handling and execution records.
Explore illustrative solutions for market entry, fragmented operations and inconsistent product knowledge, with scoped deliverables and practical acceptance criteria.
Bring a sample task, anonymized source material and an example of a good result. We can discuss the application, review requirements and evidence needed for a useful pilot.