BUYER GUIDE
AI Project Buyer Guide
Compare workflows, agents and orchestration. Assess integration readiness, ownership, evaluation and the true cost of operation.
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Build a better brief before you build a system.
Practical guides for choosing a useful AI project, describing its workflow and asking the right delivery questions.
REIT Limited · Bangladesh-based AI services and technical-delivery company
PRACTICAL CHECKLIST
Workflow Scoping Checklist
Describe one trigger, the inputs, systems, exceptions, approvals and output. Use the interactive checklist or print it for your team.
Use the scoping checklistUseful starting principles
Clarity helps people—and search systems.
Define the entity
Use consistent company and service names. Explain exactly what is offered and where the delivery boundary sits.
Answer a real decision
A definition, comparison or worked example should help a buyer choose a next step. More keyword pages do not create better evidence.
Support consequential claims
Use approved sources, accurate examples and real measurement where available. Search visibility and AI citations are observed outcomes, not promised results.
These principles also inform the AI marketing service. Google’s published guidance treats its AI search features as part of ordinary search eligibility; a special AI file is not a prerequisite.
Read Google’s AI search guidanceStart with one useful change
Book an AI AssessmentWhat would you like your business to do better?
Tell us about the process, the systems and the result you need. We can discuss a sensible first scope.
The possibilities, made tangibleSee what better work
Download the synthetic datasetSee what better work
could look like.
Fictional cases with transparent numbers. Switch scenarios to explore a different starting point.
Fictional case study · Northstar Service DeskSynthetic data · not a REIT Limited client result
enquiries / illustrative scenario
Service enquiries, ready for the CRM
A fictional technical-services company moves email enquiries into a shared CRM. Copying, routing and drafting happen in separate tools. A coordinator has to reconstruct context before replying.
- Proposed workflow
- Validate fields · Check duplicates · Draft for review · Update the CRM
- Human control
- A sales coordinator approves external messages and handles missing information.
Human effort per month
Hours · review included0160 hours
92 hmodelled capacity change / month
57.5% less human effort under these assumptions. This is not cash savings.
Workload mix
- Complete fields70%
- Missing context20%
- Duplicate candidates10%
Assumed distribution, not measured activity.
Inspect the data, calculation and pilot decision
| Input or calculated measure | Illustrative value |
|---|---|
| Monthly in-scope volume | 1,200 enquiries |
| Before: human minutes per item | 8 |
| Proposed: human minutes per item | 3 |
| Additional operating hours per month | 8 |
| Baseline human hours per month | 160 |
| Proposed human hours per month | 68 |
| Net capacity change per month | 92 hours |
| Illustrative pilot window | 4 weeks; not a delivery commitment |
| Assumed mix: Complete fields | 840 items |
| Assumed mix: Missing context | 240 items |
| Assumed mix: Duplicate candidates | 120 items |
Calculation: 1,200 × 8 ÷ 60 = 160 baseline hours. Proposed: 1,200 × 3 ÷ 60 + 8 operating hours = 68 hours.
Pilot decision: Can each enquiry reach the correct owner without duplicate records, with review time included?
Limit: Capacity released is useful only if it can be redeployed. This model does not assume higher sales.
Fictional worked example. Future human handling includes review, exceptions and corrections; operating hours are additional. Compare the same workload before drawing a conclusion.
Fictional case study · Meridian Help StudioSynthetic data · not a REIT Limited client result
support requests / illustrative scenario
Support answers with a source behind them
A fictional software-support team works from approved product articles. Reviewers search several documents before preparing each answer. Unsupported questions need a clear escalation path.
- Proposed workflow
- Read the request · Retrieve approved sources · Prepare a cited draft · Review and respond
- Human control
- A support reviewer checks the sources and approves the final answer.
Human effort per month
Hours · review included0180 hours
69 hmodelled capacity change / month
38.3% less human effort under these assumptions. This is not cash savings.
Workload mix
- Known topics60%
- Context-dependent30%
- Escalation cases10%
Assumed distribution, not measured activity.
Inspect the data, calculation and pilot decision
| Input or calculated measure | Illustrative value |
|---|---|
| Monthly in-scope volume | 900 support requests |
| Before: human minutes per item | 12 |
| Proposed: human minutes per item | 7 |
| Additional operating hours per month | 6 |
| Baseline human hours per month | 180 |
| Proposed human hours per month | 111 |
| Net capacity change per month | 69 hours |
| Illustrative pilot window | 5 weeks; not a delivery commitment |
| Assumed mix: Known topics | 540 items |
| Assumed mix: Context-dependent | 270 items |
| Assumed mix: Escalation cases | 90 items |
Calculation: 900 × 12 ÷ 60 = 180 baseline hours. Proposed: 900 × 7 ÷ 60 + 6 operating hours = 111 hours.
Pilot decision: Do sources support the answer, and are correction effort and reopened requests included?
Limit: Faster drafts are not a benefit if answer quality deteriorates. No autonomous customer reply is assumed.
Fictional worked example. Future human handling includes review, exceptions and corrections; operating hours are additional. Compare the same workload before drawing a conclusion.
Fictional case study · Bridgeway OperationsSynthetic data · not a REIT Limited client result
service requests / illustrative scenario
One request across three connected systems
A fictional B2B service team connects an inbox, CRM and fulfilment queue. Handoffs lose context. Repeated events can create duplicate work and failed updates need an owner.
- Proposed workflow
- Receive an event · Share the request state · Wait for approval · Commit and observe
- Human control
- The process owner resolves conflicting states and approves consequential actions.
Human effort per month
Hours · review included0150 hours
80 hmodelled capacity change / month
53.3% less human effort under these assumptions. This is not cash savings.
Workload mix
- Routine route60%
- Approval needed30%
- Recovery cases10%
Assumed distribution, not measured activity.
Inspect the data, calculation and pilot decision
| Input or calculated measure | Illustrative value |
|---|---|
| Monthly in-scope volume | 600 service requests |
| Before: human minutes per item | 15 |
| Proposed: human minutes per item | 6 |
| Additional operating hours per month | 10 |
| Baseline human hours per month | 150 |
| Proposed human hours per month | 70 |
| Net capacity change per month | 80 hours |
| Illustrative pilot window | 6 weeks; not a delivery commitment |
| Assumed mix: Routine route | 360 items |
| Assumed mix: Approval needed | 180 items |
| Assumed mix: Recovery cases | 60 items |
Calculation: 600 × 15 ÷ 60 = 150 baseline hours. Proposed: 600 × 6 ÷ 60 + 10 operating hours = 70 hours.
Pilot decision: Can repeated or failed events recover without duplicate work or lost request state?
Limit: Lower human effort does not automatically mean shorter customer waiting time. Vendor availability still matters.
Fictional worked example. Future human handling includes review, exceptions and corrections; operating hours are additional. Compare the same workload before drawing a conclusion.
Fictional case study · Brightfield Campaign TeamSynthetic data · not a REIT Limited client result
content assets / illustrative scenario
A clear path from campaign brief to approval
A fictional technical-services marketing team prepares 48 assets in a representative month. Drafting is fast, but facts, source material, editorial comments and channel versions become scattered.
- Proposed workflow
- Load the approved brief · Prepare channel drafts · Review facts and voice · Approve publication
- Human control
- An editor retains factual review and publication authority.
Human effort per month
Hours · review included072 hours
24 hmodelled capacity change / month
33.3% less human effort under these assumptions. This is not cash savings.
Workload mix
- Core articles62.5%
- Channel adaptations25%
- Complex revisions12.5%
Assumed distribution, not measured activity.
Inspect the data, calculation and pilot decision
| Input or calculated measure | Illustrative value |
|---|---|
| Monthly in-scope volume | 48 content assets |
| Before: human minutes per item | 90 |
| Proposed: human minutes per item | 55 |
| Additional operating hours per month | 4 |
| Baseline human hours per month | 72 |
| Proposed human hours per month | 48 |
| Net capacity change per month | 24 hours |
| Illustrative pilot window | 4 weeks; not a delivery commitment |
| Assumed mix: Core articles | 30 items |
| Assumed mix: Channel adaptations | 12 items |
| Assumed mix: Complex revisions | 6 items |
Calculation: 48 × 90 ÷ 60 = 72 baseline hours. Proposed: 48 × 55 ÷ 60 + 4 operating hours = 48 hours.
Pilot decision: Does an accepted asset require less total effort at the same editorial standard?
Limit: These figures describe content operations. They do not imply more traffic, rankings, leads or revenue.
Fictional worked example. Future human handling includes review, exceptions and corrections; operating hours are additional. Compare the same workload before drawing a conclusion.
Fictional case study · Oakline Application SupportSynthetic data · not a REIT Limited client result
support tickets / illustrative scenario
Triage that helps the technician start sooner
A fictional business supports a defined set of existing applications. Recurring tickets arrive without enough context. Technicians repeat the same initial checks.
- Proposed workflow
- Capture the symptoms · Classify the ticket · Prepare approved checks · Technician resolves
- Human control
- A technician authorises configuration and access changes.
Human effort per month
Hours · review included080 hours
12 hmodelled capacity change / month
15% less human effort under these assumptions. This is not cash savings.
Workload mix
- Routine incidents60%
- Access questions25%
- Vendor escalation15%
Assumed distribution, not measured activity.
Inspect the data, calculation and pilot decision
| Input or calculated measure | Illustrative value |
|---|---|
| Monthly in-scope volume | 160 support tickets |
| Before: human minutes per item | 30 |
| Proposed: human minutes per item | 24 |
| Additional operating hours per month | 4 |
| Baseline human hours per month | 80 |
| Proposed human hours per month | 68 |
| Net capacity change per month | 12 hours |
| Illustrative pilot window | 4 weeks; not a delivery commitment |
| Assumed mix: Routine incidents | 96 items |
| Assumed mix: Access questions | 40 items |
| Assumed mix: Vendor escalation | 24 items |
Calculation: 160 × 30 ÷ 60 = 80 baseline hours. Proposed: 160 × 24 ÷ 60 + 4 operating hours = 68 hours.
Pilot decision: Does human effort decline without increasing repeat incidents or unresolved tickets?
Limit: A modest benefit may favour an existing tool or process change. No response-time or support-hours commitment is implied.
Fictional worked example. Future human handling includes review, exceptions and corrections; operating hours are additional. Compare the same workload before drawing a conclusion.
Fictional case study · Clearpath AI OperationsSynthetic data · not a REIT Limited client result
investigations / illustrative scenario
Make AI exceptions easier to investigate
A fictional team operates a bounded internal knowledge assistant. Operators review traces without a consistent link to model versions, evaluation cases and release decisions.
- Proposed workflow
- Group related traces · Identify the version · Run evaluation cases · Review the release
- Human control
- A release owner approves prompt, model and retrieval changes.
Human effort per month
Hours · review included030 hours
6 hmodelled capacity change / month
20% less human effort under these assumptions. This is not cash savings.
Workload mix
- Knowledge issues50%
- Tool failures30%
- Policy checks20%
Assumed distribution, not measured activity.
Inspect the data, calculation and pilot decision
| Input or calculated measure | Illustrative value |
|---|---|
| Monthly in-scope volume | 40 investigations |
| Before: human minutes per item | 45 |
| Proposed: human minutes per item | 30 |
| Additional operating hours per month | 4 |
| Baseline human hours per month | 30 |
| Proposed human hours per month | 24 |
| Net capacity change per month | 6 hours |
| Illustrative pilot window | 4 weeks; not a delivery commitment |
| Assumed mix: Knowledge issues | 20 items |
| Assumed mix: Tool failures | 12 items |
| Assumed mix: Policy checks | 8 items |
Calculation: 40 × 45 ÷ 60 = 30 baseline hours. Proposed: 40 × 30 ÷ 60 + 4 operating hours = 24 hours.
Pilot decision: Can operators investigate exceptions while keeping regressions and action permissions controlled?
Limit: Risk reduction and traceability may matter more than time savings, but no invented avoided-loss value is assigned.
Fictional worked example. Future human handling includes review, exceptions and corrections; operating hours are additional. Compare the same workload before drawing a conclusion.
Fictional case study · Horizon Knowledge TeamSynthetic data · not a REIT Limited client result
knowledge requests / illustrative scenario
Find the right answer in approved knowledge
A fictional service operation maintains a permission-controlled document collection. People search across documents and must check whether the answer is current and accessible to the requester.
- Proposed workflow
- Apply access rules · Retrieve supporting passages · Draft with citations · Escalate uncertainty
- Human control
- Document owners maintain sources; users verify answers before consequential use.
Human effort per month
Hours · review included0225 hours
117 hmodelled capacity change / month
52% less human effort under these assumptions. This is not cash savings.
Workload mix
- Supported answers70%
- Clarification needed20%
- No sufficient source10%
Assumed distribution, not measured activity.
Inspect the data, calculation and pilot decision
| Input or calculated measure | Illustrative value |
|---|---|
| Monthly in-scope volume | 1,500 knowledge requests |
| Before: human minutes per item | 9 |
| Proposed: human minutes per item | 4 |
| Additional operating hours per month | 8 |
| Baseline human hours per month | 225 |
| Proposed human hours per month | 108 |
| Net capacity change per month | 117 hours |
| Illustrative pilot window | 5 weeks; not a delivery commitment |
| Assumed mix: Supported answers | 1,050 items |
| Assumed mix: Clarification needed | 300 items |
| Assumed mix: No sufficient source | 150 items |
Calculation: 1,500 × 9 ÷ 60 = 225 baseline hours. Proposed: 1,500 × 4 ÷ 60 + 8 operating hours = 108 hours.
Pilot decision: Can a permitted user find a supported answer without exposing restricted information?
Limit: The model includes maintenance effort. It does not imply error-free answers or full question coverage.
Fictional worked example. Future human handling includes review, exceptions and corrections; operating hours are additional. Compare the same workload before drawing a conclusion.
Fictional case study · Pinecrest Document OperationsSynthetic data · not a REIT Limited client result
documents / illustrative scenario
Turn routine documents into reviewable records
A fictional operations team extracts reference fields from service documents. Repeated extraction and field checks absorb time, while incomplete and conflicting documents need human judgement.
- Proposed workflow
- Read allowed fields · Check the format · Flag exceptions · Approve the record
- Human control
- A reviewer checks exceptions and approves updates to the destination system.
Human effort per month
Hours · review included0150 hours
54 hmodelled capacity change / month
36% less human effort under these assumptions. This is not cash savings.
Workload mix
- Standard format70%
- Missing fields20%
- Conflicting fields10%
Assumed distribution, not measured activity.
Inspect the data, calculation and pilot decision
| Input or calculated measure | Illustrative value |
|---|---|
| Monthly in-scope volume | 900 documents |
| Before: human minutes per item | 10 |
| Proposed: human minutes per item | 6 |
| Additional operating hours per month | 6 |
| Baseline human hours per month | 150 |
| Proposed human hours per month | 96 |
| Net capacity change per month | 54 hours |
| Illustrative pilot window | 5 weeks; not a delivery commitment |
| Assumed mix: Standard format | 630 items |
| Assumed mix: Missing fields | 180 items |
| Assumed mix: Conflicting fields | 90 items |
Calculation: 900 × 10 ÷ 60 = 150 baseline hours. Proposed: 900 × 6 ÷ 60 + 6 operating hours = 96 hours.
Pilot decision: Do records meet field-level checks, including exception handling and correction effort?
Limit: Document complexity changes the average. Compare like-for-like inputs before drawing a conclusion.
Fictional worked example. Future human handling includes review, exceptions and corrections; operating hours are additional. Compare the same workload before drawing a conclusion.
Methods worth checking
Clear evidence. Readable answers.
The examples are fictional. These independent references explain the publishing, accessibility and AI-risk principles behind the approach.
- Google Search CentralAI features and your website
Useful, accessible content and ordinary search eligibility matter. No special AI file guarantees inclusion.
- W3C Web Accessibility InitiativeMaking complex images accessible
Charts need a useful text equivalent. Every example includes values, labels and an inspectable table.
- NISTAI Risk Management Framework
A reference for considering risk, measurement and management. Referencing it is not a certification claim.