API Solutions: Connect Capabilities to Real Workflows
Start with the outcome your product needs to deliver, then connect the capabilities required to reach it. ApiDepot solution patterns show how APIs can fit together in a complete workflow, including the application rules and review steps around them. Use these examples to frame discovery and identify what needs evaluation. They are starting points for architecture and provider comparison: adapt the inputs, permissions, timing, and acceptance criteria to the people who will use your application.
Create a support workflow with clear handoffs
A support workflow might classify an incoming message, retrieve the relevant product guidance, and prepare a draft response for a team member. Treat those as separate steps with distinct acceptance criteria. The classifier needs a stable label set, retrieval needs useful source passages, and the draft needs to remain grounded in the customer’s actual question.
Decide when the workflow should ask for missing information or route the case to a person. Keep account access and any customer-record changes under explicit application permissions. Evaluate the whole sequence with difficult examples as well as ordinary messages. The useful outcome is a support task completed accurately, with a clear record of each handoff.
Turn documents into reviewable records
Document workflows can combine file handling, text extraction, field identification, and validation before information reaches a business system. Define the fields you need and the original source that supports each value. Preserve missing or uncertain information so the application can request review instead of silently turning a guess into a permanent record.
Include documents with different layouts, corrections, and incomplete details in the pilot. Check the meaning of values as well as their format, particularly identifiers, quantities, dates, and units. Decide how a reviewer sees the source beside the proposed result. This makes the final handoff understandable and helps isolate problems when extraction and validation disagree.
Connect and enrich business data deliberately
A data workflow may retrieve an existing record, look up additional information, normalize selected fields, and update a destination system. Begin by defining which source is authoritative for each field. Keep stable identifiers and record where new values came from. Establish how the workflow handles a missing match, several plausible matches, or conflicting information.
Plan pagination, checkpoints, and duplicate prevention before a large import. Separate an initial backfill from routine updates so each can use an appropriate timing and recovery policy. Evaluate the cost of supporting transformations alongside API consumption. A successful integration preserves useful data relationships and can explain why a particular record changed.
Build search that returns usable evidence
A search workflow begins with a trustworthy collection and a clear definition of relevance. Prepare source material, preserve document identities and access rules, then compare retrieval approaches using representative questions. Semantic retrieval can be evaluated alongside exact matching and structured filters when product codes, names, or version requirements matter.
Keep source passages available so users can inspect the context behind a result. If the application adds a generated answer, evaluate that answer separately from the retrieval step and make unsupported questions a deliberate case. The embeddings guide explains the path from text preparation to a maintainable index. A useful search experience helps people find evidence they are allowed to use.
Find your next
API connection.
Browse the provider guides, narrow your shortlist, and open the resources that help you plan the next step.