[{"content":" One person. Hands-on. No overhead. # I am Tao. I run Xcelerent as a solo practice based in Sydney, Australia, working with teams across Australia, Singapore, and the broader Asia-Pacific region.\nFounder, Xcelerent | AI-Enabled Outcome Owner\nConnect with me on LinkedIn\nI spent years inside corporate finance and data roles — the unglamorous centre of how numbers actually get produced. That is where I learned that the biggest wins are rarely “more strategy” and almost always less repetitive computer work done by smart people who should be deciding, not retyping.\nWhat I have shipped before # Billing automation that survived audit. Commission and revenue models that outlived three reorganisations. Document and data pipelines that connected systems marketing promised were “integrated.” More recently: LLM-backed workflows where the point is not the model — it is the hours returned to the team.\nCredentials and tools # AI / LLMs — workflow-first engineering: prompts, RAG where appropriate, local inference when the data cannot leave the building. MarkLogic — Certified Administrator and Certified Developer. Microsoft data platform — certified Azure Data Engineer and Fabric Analytics Engineer. KNIME — implementations, migrations from Alteryx and Excel, production scheduling. Python and data engineering — glue code, APIs, extraction, testing — whatever gets the job done without gold-plating. How I work # Deploy, do not advise. I am there to put working systems in place — not to rent you my vocabulary. Transfer, do not lock in. You should own what we build. Documentation and handover are part of the scope, not an extra invoice. Measure, do not assume. If we cannot point to time saved or risk reduced, we have not finished. Products # DocMark.md — AI-powered document branding compliance — is a product I build under its own brand (deep navy / teal). It is separate from Xcelerent’s consulting voice, but it comes from the same obsession with documents done properly.\nMore products are in the works; this site stays focused on hands-on workflow engineering.\nRequest a discovery call · Email lingtao@xcelerent.com\n","externalUrl":null,"permalink":"/about/","section":"AI workflow value check Sydney | Xcelerent","summary":"One person. Hands-on. No overhead. # I am Tao. I run Xcelerent as a solo practice based in Sydney, Australia, working with teams across Australia, Singapore, and the broader Asia-Pacific region.\n","title":"About Tao","type":"page"},{"content":" AI-assisted workflow engineering\nAI workflows that create measurable business value. Start with a quick value check, then book a free discovery call if your workflow looks promising. I help Australian teams reduce manual work, improve turnaround, and prove one useful workflow before scaling.\nEstimate Your AI Opportunity View Packages How it works # 1Choose one workflowPick a repeated process with manual work, delays, rework, or missed opportunity.\n2Complete the quick checkUse rough numbers. The goal is a practical starting point, not perfect accounting.\n3Review the estimateSee an indicative value range, confidence level, and suggested next step.\n4Prepare if it looks promisingUse the fuller self-value assessment for higher-value or higher-risk workflows.\n5Book the free callThe 30-minute call starts with your workflow and value assumptions, not generic AI talk.\nTop business value areas # Time savings and capacityReduce repeatable manual effort so the team can handle more work without adding headcount.\nExample:Drafting monthly report commentary from source data.\nSelf-check:How many hours does this workflow consume each month?\nFaster turnaroundShorten response, review, reporting, quoting, or processing cycles.\nExample:Preparing first-pass quote packs from CRM and document inputs.\nSelf-check:What delay would customers or internal teams notice if it improved?\nError and rework reductionCut avoidable checking, copy-paste mistakes, formatting issues, and missed handoffs.\nExample:Checking documents against a repeatable compliance or brand rule set.\nSelf-check:What does rework cost in time, risk, or reputation?\nRevenue or gross profit upliftImprove lead qualification, sales preparation, conversion support, or throughput where value can be measured.\nExample:Researching and qualifying inbound enquiries faster.\nSelf-check:Can you measure gross profit impact rather than only revenue?\nKnowledge accessMake policies, past work, documents, and decisions easier to find and apply with human review.\nExample:A controlled internal assistant for operational procedures.\nSelf-check:Where does the team lose time looking for the right answer?\nQuick Value Check\nEstimate one workflow before you book. Rough estimates are fine. This tool runs in your browser and does not send, store, or submit the entries anywhere.\nBusiness name Contact email Workflow name Main pain point Monthly volume Minutes per item People involved Staff cost range A$45-A$85/hour A$85-A$125/hour A$125-A$185/hour A$185+/hour Expected automation Conservative: 25% Moderate: 40% Strong: 60% Monthly rework/error cost Monthly gross profit impact Data readiness Weak or unclear Partial baseline Good baseline Complexity Simple workflow Moderate workflow Multi-system workflow High-risk or regulated Systems, data, or risks View Estimated Value View Packages This is an indicative estimate, not a binding quote. Success fees and value-share terms are not calculated from unverified website inputs.\nIndicative Result\nRecommended next step Estimated annual valueA$0 ConfidenceMedium Indicative investmentDiscovery first Estimated ROI multipleReview needed Thank you. You can now book a free 30-minute discovery call. If the workflow looks promising, I may ask for the fuller self-value assessment before or immediately after booking.\nBook Free Discovery Call Fuller Assessment\nUse this when the workflow looks promising. The fuller assessment prepares the discovery call around real value. It should include the workflow, current volume and effort, estimated business value, one proposed success metric, baseline data, systems involved, risks, and questions for the call.\nPrivacy Boundary\nKeep sensitive details out of public tools. Please do not submit sensitive personal information, confidential customer data, regulated information, passwords, secrets, or proprietary documents in website forms or public AI tools. Use summaries and rough estimates.\nSample prompts for your own notes Describe the workflow I want to assess whether an AI workflow could create value for my business. Help me describe this workflow clearly: - workflow name - department or team - who performs the work today - how often it happens - systems, documents, emails, or data involved - current pain points - what a better version would look like Ask follow-up questions, then produce a clear workflow summary. Estimate time and cost Use this workflow summary: [paste summary] Ask me for missing numbers, then estimate people involved, hours per week, fully loaded hourly cost, monthly labour cost, annual labour cost, and conservative/moderate/optimistic time-saving estimates. Define the success metric Help me define one primary success metric for this AI workflow. Suggest the metric, current baseline, target improvement, data source, measurement period, risks, assumptions, and exclusions. Packages # A$3k-A$6k\nAI Quick Diagnostic Check whether a smaller or uncertain opportunity is worth pursuing.\nLight workflow reviewRough value estimateFit/no-fit recommendation Value fit: A$30k-A$75k/year Run a Quick Diagnostic A$7.5k-A$18k\nAI Value Finder Discover the safest, highest-value AI opportunity before committing to a build.\nWorkflow value mapRisk and feasibility reviewRoadmap and prototype sketch Value fit: A$75k-A$150k/year+ Discuss AI Value Finder A$25k-A$75k\nAgentic Value Pilot Prove one AI workflow creates measurable value with real users and human approval points.\nOne working pilotBaseline and before/after measurement30 days of support Value fit: A$150k-A$450k/year+ Discuss a Pilot Retainer\nManaged AI Value Partner Operate, monitor, improve, and govern AI workflows over time.\nA$30k-A$120k setupA$8k-A$30k/monthOptional capped value share with sunset Value fit: A$750k-A$2m/year+ Discuss Managed AI Optional success fees or value-share terms require verified baseline data, one primary success metric, a written value measurement schedule, caps, review periods, and professional legal/commercial review. They are not calculated from website inputs.\nBooking gate # Complete the quick value check first so your free discovery call starts with your workflow, not generic AI talk. If your workflow looks promising, I may ask for the fuller self-value assessment before or immediately after booking.\nBook a free 30-minute discovery call\nFAQ # Why complete the quick value check first? It makes the discovery call practical. We can talk about one workflow, rough value, risks, and the right next step instead of starting with vague AI possibilities.\nWhat if I do not know the numbers? Use rough estimates. The estimate is a conversation starter, not a quote or a measurement baseline.\nCan I use ChatGPT to help complete it? Yes, but do not paste sensitive personal information, confidential customer data, regulated information, passwords, secrets, or proprietary documents into public AI tools.\nIs the calculator a final quote? No. It is indicative only. Final pricing depends on scope, data readiness, risk, systems, integrations, baseline validation, and support requirements.\nDo you charge by the hour? No. Agentic AI engineering is priced around the value case, delivery risk, and scoped outcome. I may use time internally to check delivery sustainability, but I do not sell the work as developer hours.\nWhat if my workflow is not suitable for AI? I will say so. The right answer may be no fit, a smaller diagnostic, deterministic automation, data cleanup first, or revisiting the workflow later.\nAre prices in Australian dollars? Is GST included? Prices are in Australian dollars and exclude GST unless stated otherwise. Public pricing, GST wording, privacy notices, and standard terms should be reviewed before launch or contract use.\n","externalUrl":null,"permalink":"/services/ai-deployment/","section":"Services","summary":" AI-assisted workflow engineering\nAI workflows that create measurable business value. Start with a quick value check, then book a free discovery call if your workflow looks promising. I help Australian teams reduce manual work, improve turnaround, and prove one useful workflow before scaling.\n","title":"AI workflow value check","type":"services"},{"content":" AI workflow value check\nAI workflows that create measurable business value. Start with one workflow, one rough value estimate, and one practical next step. I help Australian teams find, build, and govern AI workflows that reduce manual work, improve turnaround, and keep humans in control.\nEstimate Your AI Opportunity Explore Custodara Start with the value check # 1Choose one workflowPick a repeated process with manual work, delays, rework, knowledge gaps, or missed opportunity.\n2Use rough numbersEstimate volume, effort, rework, margin impact, data readiness, and complexity. Precision can come later.\n3See the likely next stepThe result suggests whether to pause, diagnose, map value, pilot, or manage a larger AI workflow program.\n4Book only if it fitsThe free discovery call starts with your workflow and value assumptions, not generic AI theatre.\nQuick Value Check\nEstimate one workflow before you book. Rough estimates are fine. This tool runs in your browser and does not send, store, or submit the entries anywhere.\nBusiness name Contact email Workflow name Main pain point Monthly volume Minutes per item People involved Staff cost range A$45-A$85/hour A$85-A$125/hour A$125-A$185/hour A$185+/hour Expected automation Conservative: 25% Moderate: 40% Strong: 60% Monthly rework/error cost Monthly gross profit impact Data readiness Weak or unclear Partial baseline Good baseline Complexity Simple workflow Moderate workflow Multi-system workflow High-risk or regulated Systems, data, or risks View Estimated Value View Packages This is an indicative estimate, not a binding quote. Success fees and value-share terms are not calculated from unverified website inputs.\nIndicative Result\nRecommended next step Estimated annual valueA$0 ConfidenceMedium Indicative investmentDiscovery first Estimated ROI multipleReview needed Thank you. You can now book a free 30-minute discovery call. If the workflow looks promising, I may ask for the fuller self-value assessment before or immediately after booking.\nBook Free Discovery Call Fuller Assessment\nUse this when the workflow looks promising. The fuller assessment prepares the discovery call around real value. It should include the workflow, current volume and effort, estimated business value, one proposed success metric, baseline data, systems involved, risks, and questions for the call.\nPrivacy Boundary\nKeep sensitive details out of public tools. Please do not submit sensitive personal information, confidential customer data, regulated information, passwords, secrets, or proprietary documents in website forms or public AI tools. Use summaries and rough estimates.\nSample prompts for your own notes Describe the workflow I want to assess whether an AI workflow could create value for my business. Help me describe this workflow clearly: - workflow name - department or team - who performs the work today - how often it happens - systems, documents, emails, or data involved - current pain points - what a better version would look like Ask follow-up questions, then produce a clear workflow summary. Estimate time and cost Use this workflow summary: [paste summary] Ask me for missing numbers, then estimate people involved, hours per week, fully loaded hourly cost, monthly labour cost, annual labour cost, and conservative/moderate/optimistic time-saving estimates. Define the success metric Help me define one primary success metric for this AI workflow. Suggest the metric, current baseline, target improvement, data source, measurement period, risks, assumptions, and exclusions. Where value usually appears # Time savings and capacityReduce repeatable manual effort so the team can handle more work without adding headcount.\nGood first workflow:Monthly reporting, document packet assembly, recurring checks, intake triage.\nFaster turnaroundShorten response, review, reporting, quoting, or processing cycles where delays have visible business cost.\nGood first workflow:Quote preparation, approval packs, customer response drafts, compliance review queues.\nError and rework reductionCut avoidable checking, copy-paste mistakes, formatting issues, missed handoffs, and inconsistent review.\nGood first workflow:Document checks, data extraction, exception notes, policy or brand compliance review.\nKnowledge accessMake policies, procedures, past work, documents, and decisions easier to find and apply with human review.\nGood first workflow:Governed internal assistants, searchable knowledge repositories, evidence-backed answers.\nPackages # A$3k-A$6k\nAI Quick Diagnostic Check whether a smaller or uncertain opportunity is worth pursuing.\nLight workflow reviewRough value estimateFit/no-fit recommendation Value fit: A$30k-A$75k/year Run the value check A$7.5k-A$18k\nAI Value Finder Find the safest, highest-value AI opportunity before committing to a build.\nWorkflow value mapRisk and feasibility reviewRoadmap and prototype sketch Value fit: A$75k-A$150k/year+ Discuss Value Finder A$25k-A$75k\nAgentic Value Pilot Prove one AI workflow creates measurable value with real users and human approval points.\nOne working pilotBaseline and before/after measurement30 days of support Value fit: A$150k-A$450k/year+ Discuss a pilot Retainer\nManaged AI Value Partner Operate, monitor, improve, and govern AI workflows over time.\nA$30k-A$120k setupA$8k-A$30k/monthOptional capped value share with sunset Value fit: A$750k-A$2m/year+ Discuss managed AI Optional success fees or value-share terms require verified baseline data, one primary success metric, a written measurement schedule, caps, review periods, and professional legal/commercial review. They are not calculated from website inputs.\nCustodara: wiki-to-Git migration assessment # CU Custodara Assess how to move wiki knowledge into governed Markdown and Git, with links, assets, ownership, and review evidence preserved.\nAI Full value-check page Read the fuller AI workflow offer, prompts, FAQ, and booking guidance.\nML MarkLogic consulting Document search, discovery, semantics, clusters, performance, security, upgrades, and migrations.\nKN KNIME implementation Alteryx to KNIME, VBA to KNIME, or net-new data workflows your team can read and own.\nHow I work # 1 Find the value We start with the workflow, baseline, risk, and value case before choosing a model or tool.\n2 Build the smallest useful system I build with tools your team can support: Microsoft 365, Python, APIs, KNIME, MarkLogic, local models, or existing systems.\n3 Govern and transfer ownership Human review, permission boundaries, documentation, and handover matter as much as the automation.\nStart with one workflow. Use the value check above, then book a free 30-minute discovery call if the opportunity looks real.\nBook a free discovery call Or email lingtao@xcelerent.com · Sydney, Australia\n","externalUrl":null,"permalink":"/","section":"AI workflow value check Sydney | Xcelerent","summary":" AI workflow value check\nAI workflows that create measurable business value. Start with one workflow, one rough value estimate, and one practical next step. I help Australian teams find, build, and govern AI workflows that reduce manual work, improve turnaround, and keep humans in control.\n","title":"AI workflow value check Sydney | Xcelerent","type":"page"},{"content":"Notes on shipping AI into real workflows — without the hype.\nThis section is intentionally quiet until there is something useful to publish: field notes, practical how-tos, and case studies only when the source material is confirmed. Subscribe via RSS for the full site feed.\n","externalUrl":null,"permalink":"/blog/","section":"Blog","summary":"Notes on shipping AI into real workflows — without the hype.\nThis section is intentionally quiet until there is something useful to publish: field notes, practical how-tos, and case studies only when the source material is confirmed. Subscribe via RSS for the full site feed.\n","title":"Blog","type":"blog"},{"content":"","externalUrl":null,"permalink":"/categories/","section":"Categories","summary":"","title":"Categories","type":"categories"},{"content":" Let us talk about your workflows # If your team lives in spreadsheets, PDFs, approvals, and handoffs — and you suspect AI or better automation could help — start here. The first call is 30 minutes, no charge, and no pitch deck. We figure out whether there is a useful workflow to build and what “observe first” would look like.\nRequest a discovery call # Book a discovery call Choose a time for a free 30-minute discovery call. The scheduler opens in a new tab and checks availability before confirming. Book a discovery call Please do not include sensitive client data, credentials, or confidential documents in the booking notes. Send a message with a little context and I will reply with a few possible times for the free 30-minute discovery call. If email is easier, write directly to lingtao@xcelerent.com.\nHelpful context to include, if you know it:\nthe recurring workflow or handoff you want to improve the tools or systems involved today where time leaks, rework, or risk show up high-level constraints such as approvals, data residency, or hosting urgency, budget range, or how you found Xcelerent, if useful Send a message # Name Email Message Optional: mention the workflow, current tools, main time leak, and any high-level constraints. Send message I use your message only to reply and decide whether there is a sensible next step. The form is handled by Formspree, so avoid sending sensitive client data, credentials, or confidential documents here.\nDirect contact # Email: lingtao@xcelerent.com Location: Sydney, Australia (AEST / AEDT) What to expect on the call # We walk through what your team does today, where time leaks, and what constraints matter — data residency, tool approvals, risk. If there is a sensible first slice of work, I will describe what an observe → design → build → transfer engagement looks like for your context. If not, I will say so.\nServices · About\n","externalUrl":null,"permalink":"/contact/","section":"AI workflow value check Sydney | Xcelerent","summary":"Let us talk about your workflows # If your team lives in spreadsheets, PDFs, approvals, and handoffs — and you suspect AI or better automation could help — start here. The first call is 30 minutes, no charge, and no pitch deck. We figure out whether there is a useful workflow to build and what “observe first” would look like.\n","title":"Contact Xcelerent","type":"page"},{"content":"Move your wiki to Git. Keep it trustworthy.\nCustodara is Xcelerent\u0026rsquo;s wiki-to-Git migration assessment for teams whose operating knowledge is stuck in wiki pages, attachments, outdated links, ownership gaps, and review cycles that are hard to audit.\nThe assessment does not assume a hosted platform or a big-bang migration. It maps your current wiki structure, migration risks, governance needs, and practical route into Markdown and Git so the knowledge can be reviewed, versioned, and owned.\nWho this is for # Finance, operations, compliance, reporting, product, and knowledge teams that depend on wiki content but need stronger governance:\nimportant procedures, policies, decisions, and runbooks pages with unclear owners, stale review dates, or duplicated guidance attachments and images that must survive migration internal links, cross-references, and page hierarchies that people rely on teams that want a local-first, auditable knowledge base before adding future search or AI workflows What the assessment covers # Knowledge inventory - map spaces, pages, owners, review status, attachments, and migration risk. Content model - define Markdown structure, front matter, file naming, and repository layout. Asset and link preservation - identify how attachments, images, anchors, and internal links should move. Governance design - establish ownership, review evidence, approval flow, and change history. Migration pathway - recommend a staged plan, sample conversion, acceptance checks, and handover approach. How I approach it # 1. Map the current wiki # We start with one important space or content set. I identify structure, source formats, owners, stale areas, sensitive content, and the links or assets that cannot be lost.\n2. Design the governed repository # I propose a Markdown and Git structure that your team can understand: folders, naming, front matter, review fields, ownership signals, and practical checks for broken links or missing assets.\n3. Prove the path # We convert a representative slice, review the output, record gaps, and decide whether a broader migration is worth doing now.\nGood outcomes # Teams can see what will move, what needs cleanup, and what should stay behind. Markdown files have clear ownership, review status, and change history. Attachments, images, and internal links get explicit preservation rules. Git becomes a practical governance layer, not just a developer tool. The migrated knowledge is ready for future search or AI workflows because sources and review evidence are clearer. Where MarkLogic, KNIME, and AI fit # Custodara is the assessment and migration-governance front door. If a later phase needs search, workflow automation, or document processing, Xcelerent can bring in MarkLogic, KNIME, Python, or AI-assisted review patterns. The first priority is preserving trustworthy knowledge in a format your team can own.\nBook a Wiki-to-Git Migration Assessment · Run the AI value check · All services\n","externalUrl":null,"permalink":"/services/custodara/","section":"Services","summary":"Move your wiki to Git. Keep it trustworthy.\nCustodara is Xcelerent’s wiki-to-Git migration assessment for teams whose operating knowledge is stuck in wiki pages, attachments, outdated links, ownership gaps, and review cycles that are hard to audit.\n","title":"Custodara: Wiki-to-Git Migration Assessment","type":"services"},{"content":"KNIME is the tool I reach for when a team is paying too much for ETL licensing, or when critical logic is trapped in Excel macros nobody wants to touch.\nThe problem with the status quo # Alteryx — capable, but license cost scales with headcount and ad-hoc flows become expensive quickly. VBA — fast to write, fragile to own. One broken macro becomes a single point of failure with no tests and no lineage. What I deliver # Alteryx → KNIME # I recreate workflows so behaviour matches what the business expects, document the graph, and cut the licensing line item where it makes sense. You keep the logic; you lose the per-user bill.\nVBA → KNIME # I extract the business rules from workbooks, rebuild them as visual workflows with error handling and logging, and leave something a non-coder can actually read.\nNet-new KNIME # Data cleaning, API pulls, database ETL, scheduled reporting, handoff to Python or R when you need code — all in one orchestrated graph.\nWhy KNIME # Open source — no per-user tax for people who only need to inspect or lightly edit. Visual — finance and ops stakeholders can follow the graph without reading thousands of lines of code. Integrations — databases, Excel, cloud APIs, Python, R — in one place. Examples of what I build # Financial reporting chains, commission and billing calculations, master data cleanup, recurring exports to downstream systems, and scheduled packs for leadership.\nRequest a discovery call · All services\n","externalUrl":null,"permalink":"/services/knime/","section":"Services","summary":"KNIME is the tool I reach for when a team is paying too much for ETL licensing, or when critical logic is trapped in Excel macros nobody wants to touch.\n","title":"KNIME implementation and migration","type":"services"},{"content":"Certified expertise for teams that treat documents and semantics as first-class data — not an afterthought in a relational row.\nWhat MarkLogic does # MarkLogic is Progress\u0026rsquo;s enterprise multi-model database and search platform. It is often used when teams need one place to store, search, connect, and govern mixed content: documents, metadata, JSON, XML, text, semantic triples, geospatial data, and binaries.\nIn plain English: it is useful for document discovery, document search, knowledge repositories, and applications where the important context lives across documents rather than in neat database rows.\nPublic-sector proof # Progress publishes a Defense Technical Information Center customer story describing a MarkLogic Data Hub for the U.S. Department of Defense community, with search, discovery, security, and semantic context at the centre of the work. Progress also positions MarkLogic for defense and intelligence-community data hub patterns.\nPublic procurement reporting has also listed FBI MarkLogic software awards. My own prior public-sector work includes a Revenue NSW land tax collection project.\nCredentials # MarkLogic Certified Administrator MarkLogic Certified Developer Microsoft-certified Azure Data Engineer Microsoft-certified Fabric Analytics Engineer What I help with # New implementations — data modelling, app patterns, ingestion, and query design that will not fall over at production volume. Operations — cluster health, backups, recovery drills, monitoring hooks, upgrade paths. Performance — slow queries, index strategy, resource contention, and realistic benchmarking. Security — roles, compartments, encryption posture, integration with your IdP — described in a way your security team can sign off. Search and applications — REST APIs, custom search experiences, hybrid structured + unstructured use cases. Migration — off legacy repositories or failed first attempts, with a plan that respects downtime and validation requirements. Where MarkLogic fits # Legal and compliance document repositories Financial regulatory and disclosure workloads Property and lease document stores Government policy and correspondence management If you are deciding whether MarkLogic is the right engine for a greenfield problem, I will be honest when something simpler is enough — and when it is not.\nRequest a discovery call · All services\n","externalUrl":null,"permalink":"/services/marklogic/","section":"Services","summary":"Certified expertise for teams that treat documents and semantics as first-class data — not an afterthought in a relational row.\nWhat MarkLogic does # MarkLogic is Progress’s enterprise multi-model database and search platform. It is often used when teams need one place to store, search, connect, and govern mixed content: documents, metadata, JSON, XML, text, semantic triples, geospatial data, and binaries.\n","title":"MarkLogic consulting for document search","type":"services"},{"content":"I help teams turn repeated document, reporting, search, and knowledge work into governed AI workflows that can be measured, reviewed, and owned.\nCore offerings # AI AI workflow value check Estimate one workflow, then choose the right next step: diagnostic, value finder, pilot, or managed AI partner.\nCU Custodara wiki-to-Git migration assessment Assess how to move wiki knowledge into governed Markdown and Git while preserving assets, links, owners, and review evidence.\nML MarkLogic consulting Document search, discovery, semantics, clusters, performance, security, upgrades, and migrations.\nKN KNIME implementation Practical alternative to Alteryx licensing and brittle VBA: migrations and net-new pipelines.\nNot sure what fits? Start with the value check. It gives us a concrete workflow, value hypothesis, and risk profile for the first conversation.\nEstimate your AI opportunity ","externalUrl":null,"permalink":"/services/","section":"Services","summary":"I help teams turn repeated document, reporting, search, and knowledge work into governed AI workflows that can be measured, reviewed, and owned.\nCore offerings # AI AI workflow value check Estimate one workflow, then choose the right next step: diagnostic, value finder, pilot, or managed AI partner.\n","title":"Services","type":"services"},{"content":"","externalUrl":null,"permalink":"/tags/","section":"Tags","summary":"","title":"Tags","type":"tags"}]