We design and implement AI-assisted workflows for e-commerce teams: reporting preparation, request routing and internal knowledge access. Each project connects the agreed tools, tests the output and gives the team a documented process to operate.
Discuss your prioritiesYour team is copying figures between tools, searching for internal information or manually coordinating routine requests. We identify what can be automated, where AI adds value and which decisions should remain with a person.
The weekly performance review starts with exports, spreadsheet updates and manual reconciliation.
Requests move between messages, documents and task boards without a consistent owner or status.
AI tools are being tested individually, with no shared workflow, quality checks or adoption plan.
An implementation engagement delivers a working process: connected inputs, tested outputs, operating instructions and handover. A feasibility pilot comes before a wider rollout.
Map the steps, systems and people involved. Identify repeated work, data gaps and costly exceptions. Compare potential applications by expected usefulness, feasibility and operating cost, then select a starting point.
Process map · Prioritised use cases · Pilot scopeConnect the agreed systems through available APIs or automation tools. Define field mappings, triggers, permissions and error handling. Add AI only where interpretation or generation serves the task; use deterministic rules for fixed calculations and routing logic.
Integration map · Workflow rules · Access requirementsBuild workflows that classify requests, retrieve approved knowledge, summarise changes or prepare drafts. Define the inputs, permitted sources, output format and review step. Actions with business consequences require explicitly agreed controls.
Source rules · Structured outputs · Approval stepsTest normal inputs, missing data, duplicates and failed connections. Compare outputs with agreed acceptance criteria, document how to operate the workflow and introduce it to the team. Define monitoring, support and future changes separately.
Acceptance tests · Operating guide · HandoverConsider a team that repeatedly gathers advertising and store data to prepare the same weekly review. The useful project includes the data pipeline, the checks and the final handoff.
Pull the agreed inputs, align currencies and reporting windows, and flag missing data. Calculate metrics with defined formulas before passing the result to an AI model.
Use AI to draft a summary of changes from the validated inputs. Link observations back to source figures and flag uncertainty instead of filling gaps.
A designated reviewer checks the brief before it reaches the team. Failed runs and incomplete inputs are routed for attention; the workflow does not change advertising budgets.
Illustrative project, not a client case study. Feasibility and data access are assessed before commitment.
The handover covers the workflow, its connections and its operating limits. We define what is automated, what needs approval, how failures are surfaced and who is responsible after launch.
The agreed connections, automation logic and AI components, with access and ownership documented.
The scenarios checked, expected outputs, known limitations and issues requiring follow-up.
Instructions for running, reviewing and troubleshooting the workflow, plus the agreed support scope.
The examples describe proposed applications, not achieved client results. Feasibility depends on data quality, permissions and tool capabilities. Software licences, API usage and ongoing maintenance are separate unless included in the agreement.
Reporting, request routing and internal knowledge.
Three examples of AI connected to day-to-day work.
Period, metrics, anomalies and open questions. Budget changes stay with the decision-maker.
A category, responsible team and status, with ambiguous requests held for review.
Relevant procedures and source references. Unsupported questions are escalated for clarification.
Run a reporting prototype, inspect its checks and see where AI fits.
A recurring process with accessible inputs, a clear output and a person able to review it. Reporting preparation, request triage or internal knowledge retrieval can provide a bounded starting point. We assess the actual workload and data before recommending a pilot.
Usually the first step is to assess the stack already in place: store platform, ad accounts, spreadsheets, documents and operational tools. API availability, permissions and data quality determine what can be connected. Any new subscription or migration is identified during scoping.
We agree a baseline and an acceptance measure before implementation. Depending on the workflow, this may be preparation time, manual handoffs, exception frequency or time to find an answer. The assessment includes software costs and the work still needed to review outputs.
We map the data required, the systems it passes through and the permissions needed. Provider selection, credential handling and approved knowledge sources are part of scoping. Sensitive data is only introduced where its use has been explicitly agreed.
Only within a specifically defined permission boundary. We agree which actions may be automated, which need approval and how exceptions are escalated. A reporting assistant, for example, can prepare a review without receiving permission to change campaign budgets.
The handover includes documentation and known limitations. Monitoring, provider changes, maintenance and new features require an agreed owner and support arrangement. They are not left implicit in the initial build.