AI consultancies should dominate AI search. Almost none of them do.

AI consultancies earn citations in ChatGPT, Perplexity, and Claude through three inputs: named-practitioner production write-ups tied to specific use cases, consistent GitHub and Hugging Face presence that proves the team ships, and use-case-specific structured content that matches how AI assistant buyers phrase their queries. Generic “we offer AI services” positioning contributes almost nothing to this retrieval pool, regardless of how strong the underlying delivery capability is.

The irony at the heart of AI consultancy marketing in 2026

The firms that build the systems powering AI search are almost entirely absent from it. That is what the data shows.

Of public AI services firms scanned in the 100Signals Q1 2026 firm-hub review, only approximately 4% appeared in AI-assistant citations for any of the use cases or verticals they claim to serve. The firms that ranked those use cases on their homepages, published case studies, and listed capabilities were still invisible when a buyer asked ChatGPT or Perplexity for a recommendation.

The firms with the deepest technical understanding of AI retrieval are mostly unretrievable.

The root cause is a content architecture problem. Most AI consultancy websites are structured around service taxonomy: RAG implementation, fine-tuning, agentic workflows, MLOps. That structure tells the AI assistant what the firm offers. It does not tell the assistant what the firm has shipped, at what scale, with what measured outcomes, for which specific use cases, and with which named practitioners credited.

AI assistants recommend firms based on evidence, not stated capabilities. The evidence pool for AI consultancy queries is practitioner-authored technical writing, maintained OSS repositories, Hugging Face model cards, conference speaker records at technical venues, and eval reports with named lead researchers. The typical AI consultancy website contributes almost none of this.

The cited firms are not necessarily larger or older. They publish more specifically, more often, and in the right places. The gap between 4% and the rest is a content investment gap, not a capability gap.

Why AI consultancy buyers research vendors via AI assistants more than any other ICP

The buyer for an AI consultancy in 2026 researches vendors using the same tools they are buying from vendors to deploy.

From 100Signals operator interviews and the firm-hub scan: the AI consultancy buying committee typically includes the founder or CTO who owns the technical-fit decision, a Head of AI or Head of Data who evaluates implementation feasibility, the business sponsor for the specific use case (often a VP of Operations, VP of Customer Service, or similar), and procurement appearing late for data residency and IP questions.

The CTO and Head of AI are, by definition, daily users of ChatGPT, Perplexity, and Claude. When they begin vendor research, they ask those tools directly. They do not start with Google, and they do not start with a referral call.

What they ask looks like: “best AI consultancy for RAG implementation in legal,” “who are the top ML firms for demand forecasting in retail,” “eval framework specialists for healthcare AI.” These are use-case queries with embedded verticals and technical specifics. The AI assistant that answers those queries draws from a retrieval pool of practitioner-authored technical writing, not from service pages that list capabilities.

The decision-making criterion that has shifted most since 2023 is production-deployment proof. Buyers weight “have they shipped this kind of system to production with measured outcomes” above almost everything else. Pilots, proof-of-concept write-ups, and capability statements do not clear that bar. A detailed production write-up authored by the named engineer who built it, explaining the architecture, the evals, and what failed, does.

The buyers most likely to use AI search to research vendors are the ones buying from AI consultancies. The AI consultancies most invisible in AI search are the ones pitching to those buyers. Fixing the visibility problem addresses the primary research channel of the primary buyer.

The two retrieval systems for AI consultancy queries: training corpus vs live retrieval

AI recommendation engines draw on two distinct retrieval systems. The balance between them determines whether a boutique AI consultancy gains visibility in 90 days or needs 18 months.

Training corpus retrieval is knowledge absorbed during pre-training. For AI consultancy queries, this favors firms whose practitioners have appeared in arXiv preprints, academic conference proceedings (NeurIPS, ICML), and well-indexed technical writing over multiple years. It is a slow advantage: model weights update infrequently, and training corpus presence requires a body of published work that accumulates over time.

Live retrieval is real-time web search used by Perplexity, Claude with search enabled, and ChatGPT’s browse mode. For AI consultancy queries, live retrieval is the dominant mechanism because the AI field moves faster than training cycles. A paper published at NeurIPS in December 2025 may not be in the current model weights of a platform that last updated training in August 2025. A technical essay published on dev.to last month can appear in Perplexity citations this week.

Most AI consultancies miss this. The AI services field moves fast enough that training corpus presence is always partially stale. Live retrieval rewards recency and platform diversity. A firm that publishes one named-practitioner production write-up per month, maintains an active GitHub presence, and submits to technical conferences builds live-retrieval footprint faster than training corpus weights update.

Platform behavior varies. Perplexity uses live retrieval by default and pulls heavily from GitHub READMEs and technical blog posts for AI-services queries. Claude with search enabled weights longer-form technical writing and structured benchmark data. ChatGPT with browsing weights structured technical documentation and vendor sites more than for generic B2B queries. Google AI Overviews applies E-E-A-T rules and weights named authorship strongly.

For niche use-case queries, live retrieval is the faster path. Consistent practitioner content at the right cadence on the right surfaces can move a firm from zero citations to the ExaltGrowth 6-citation threshold within 90 days on specific queries.

Where AI consultancy buyers actually search: the query and platform map

AI consultancy research queries are use-case-specific, technically precise, and often platform-named. Understanding this is the prerequisite to building visibility in the right places.

Use-case queries dominate AI consultancy buyer research, and those queries retrieve from practitioner-native technical surfaces, not from company homepages or tier-1 business publications. The firms winning these queries published specifically about those use cases with named authors.

The practitioner-native citation surfaces: the actual center of gravity for AI consultancies

For AI consultancies, tier-1 business publications, analyst directories, and NACD or AICPA conference speaker pages carry weight only for advisory-pivot archetypes. For engineer-founder shops and applied research labs, the citation surfaces that move AI consultancy queries are different.

GitHub READMEs

From an AI visibility standpoint, GitHub is a technical publishing platform. Perplexity crawls and cites GitHub READMEs directly for AI-services queries.

A citation-eligible GitHub README explains: what problem the tool solves, at what scale and in which production contexts it has been deployed, who built it and their institutional affiliation, and where it has been referenced or used. It reads like the abstract and introduction of a technical paper, not a quickstart guide.

Time-to-citation expectation for a well-structured README on a relevant repository: 2-4 weeks for Perplexity indexing if the repository has traction (stars, forks, recent commits). A zero-star repository with no commit activity is not a citation surface.

For boutique AI consultancies, the strategic question is which OSS project to start or maintain. Pick the tooling or framework that reflects your core use-case positioning. A RAG-focused firm should own a RAG evaluation harness on GitHub. A forecasting firm should maintain a time-series benchmarking tool. The OSS project is a marketing asset and a live-retrieval citation anchor.

Hugging Face model cards

Hugging Face is indexed by AI search platforms. A model card on a deployed or fine-tuned model, authored by a named practitioner with firm affiliation, is a high-weight technical citation surface for AI consultancy queries, and a Hugging Face Spaces demo buyers can evaluate directly.

An anatomy of a citation-eligible model card: problem statement with vertical specificity (not “general text classification” but “legal contract clause classification at 50k+ document scale”), training data methodology, evaluation results against named benchmarks, known limitations and failure modes, deployment context, and named practitioner authorship. Cards that read like model documentation get retrieved. Marketing copy does not.

Time-to-citation: Hugging Face is crawled by Perplexity within days of publication for relevant queries. Model cards with evaluation data and production context appear in AI consultancies citations within 2-3 weeks of publication.

Papers with Code entries

Papers with Code is a high-weight surface for AI assistants responding to AI consultancy queries. When Claude or Perplexity is asked about an eval approach or benchmark methodology, Papers with Code entries from named practitioners appear consistently.

A Papers with Code entry requires an actual implementation with code, a methodology write-up, and benchmark results. It is a citation anchor for queries about methodology, evaluation approach, and reproducibility. For AI consultancies that publish eval frameworks or benchmark reports, a Papers with Code entry for the methodology is the fastest path to retrieval on eval-specific queries.

arXiv preprints with named author affiliations

arXiv is the strongest single citation signal for training-corpus AI consultancies visibility. A preprint with named authors and firm affiliation, on a relevant AI topic, enters model training data at a higher weight than blog posts or commercial content.

Boutique AI consultancies do not need NeurIPS-track papers. Technical reports and implementation papers qualify. “Production RAG for Legal Document Review: Architecture, Evaluation, and Failure Analysis” is a legitimate arXiv preprint. arXiv accepts technical reports. Named practitioner authorship with firm affiliation is the citation anchor.

For firms with an applied research orientation (the second archetype in the ICP), arXiv preprints are the highest-leverage training-corpus investment.

dev.to and Medium engineering essays

dev.to and Medium’s engineering publications are live-retrieval citation surfaces for Perplexity and Claude. They carry lower weight for Google E-E-A-T, but Perplexity’s source patterns for AI consultancy queries pull heavily from technical blog content on these platforms.

The anatomy of a citation-eligible engineering essay: named author with linked GitHub profile and firm affiliation, specific use case with named vertical, production-scale detail (number of documents, latency measurements, model versions used), architecture decisions with explicit reasoning, what failed and why, and a clear takeaway for practitioners facing the same problem. The posts that get cited look like: “I built RAG for legal discovery at 500k documents and here is what the embedding model comparison revealed.” Generic “how to build a RAG system” posts are not retrieved.

Cadence matters. Perplexity weights content updated within 30 days at 3.2x higher citation rates than older content. A monthly named-practitioner essay maintains freshness in the live-retrieval pool.

AI Engineer Summit, MLOps Community Conf, PyData, NeurIPS, ICML, ApplyConf, Latent Space Live

Conference speaker pages from technical AI venues are high-authority third-party citation surfaces. LLMs treat them as named-expert anchors. A speaker biography page at AI Engineer Summit or MLOps Community Conf constitutes an authoritative record that AI assistants cross-reference when evaluating practitioner credentials.

The citation-eligible conference speaker page has a 200-word minimum bio, explicit use-case and vertical specialization, links to related publications and GitHub, and the speaker’s specific talk or session description. Speaker pages without substantive bios are low-weight. Bios that read as practitioner credentials tied to specific technical work are high-weight.

For AI consultancies firms, the strategy is submitting practitioners systematically to regional and community conferences where acceptance rates are higher, the audience is technically sophisticated, and the speaker pages are indexed. MLOps Community Conf, PyData (city-level), and local AI Engineer meetup recordings all generate indexed speaker pages. Each is a citation anchor.

Tier-1 business publications and adjacent leverage

HBR, MIT Sloan, Strategy+Business, and Towards Data Science carry meaningful citation weight for AI consultancy queries. For advisory-pivot AI consultancies, where the buyer is a senior executive evaluating strategic AI advisory rather than an engineer evaluating a production build, they are the center of gravity.

For engineer-founder shops and applied research labs, these publications are adjacent leverage. A well-placed HBR piece on AI transformation ROI strengthens a firm’s training-corpus footprint and adds a tier-1 citation to the entity record. It does not replace the practitioner-native surfaces that dominate retrieval for use-case queries.

The Gradient, Towards Data Science, and Import AI occupy a middle ground: they are not as authoritative as HBR for management-advisory queries, but they carry more weight than dev.to for training-corpus presence on technical AI topics. A byline in The Gradient or Towards Data Science is more retrievable for Claude and ChatGPT on AI-methodology queries than for Perplexity on use-case queries.

For advisory-pivot AI consultancies, the consulting-firms AI-vis page covers the Visible Expert pathway in detail, including the Hinge Research framework, tier-1 publication strategy, and analyst directory approach. If your firm is primarily an advisory shop, that pathway applies to you in full. See the consulting firms AI visibility guide for that approach.

For engineer-founder shops and applied research labs, treat business publications as supplementary. One strategic HBR byline per year from the firm’s most senior practitioner adds a high-quality citation anchor. It does not substitute for the practitioner-native content cadence.

Use-case-specific positioning beats vertical-specific positioning for AI consultancies

Use-case-specific positioning wins AI consultancies search visibility. “We build RAG pipelines for legal document review at 50,000-document scale, with eval frameworks tuned for legal accuracy, not benchmark accuracy” outperforms “we are an AI consultancy for legal firms” on every relevant query.

AI assistant buyers do not ask “best AI consultancy for legal.” They ask “best consultancy for RAG implementation in legal document review” or “who builds production RAG for legal discovery workflows.” The entity that appears in responses to those queries is the entity that published specifically on that use case, with specific outcomes, from named practitioners.

The pattern that compounds is: use case plus vertical plus scale. “RAG for legal” is broad. “RAG for legal document review at 50k+ documents” signals production maturity. “RAG for legal document review at 50k+ documents with RAGAS-based evaluation framework” gives AI assistants a named methodology they can reference directly.

Additional examples of the use-case-vertical-scale formula:

• “Demand forecasting for retail with SKU-level granularity across 100k+ products” outperforms “AI for retail” for buyer queries and outranks on Perplexity
• “Agentic workflow automation for insurance claims processing” outperforms “AI automation for insurance” on Claude use-case queries
• “Computer vision for manufacturing quality inspection at line speed” outperforms “computer vision consulting” for Google AI Overviews with E-E-A-T weighting
• “Eval framework design for customer service AI at enterprise scale” is a standalone query category that most AI consultancies have no content for

Big-4 firms and McKinsey-pivoted AI practices have brand recognition at the head term (“best AI consultancy 2026”). They do not have use-case-specific practitioner writing. A boutique that publishes 6-8 detailed production write-ups on specific use cases, each authored by a named practitioner, will outrank larger firms for those specific queries within a 90-day window on Perplexity and Claude.

The 6-citation threshold finding from ExaltGrowth’s 2026 cross-vertical study applies directly: for niche use-case queries, 6 citations in the retrieval pool makes a brand 6x more likely to appear in recommendations. That threshold is achievable in 90 days for a boutique that publishes specifically. For head terms, the threshold requires 18+ months of compounding. The boutique playbook is to own use-case queries first.

Required content assets for AI consultancies AI visibility

The content asset list for AI consultancy AI visibility differs from both the generic B2B services playbook and the consulting firm pathway.

Named-practitioner production case studies. The load-bearing asset. Named-engineer write-ups that explain the specific architecture, the evaluation methodology, the outcomes with measurements, and what failed. The practitioner’s name, GitHub profile, and conference bio should be linked from the case study. Firm-branded case studies that credit “our team” are not retrieved consistently. This is the single asset AI assistants pull most reliably for AI consultancy use-case queries.

Eval framework write-ups with public evaluation data. Evaluation methodology is a differentiated topic in AI services. A published eval framework for a specific use case (customer service LLM accuracy, RAG retrieval quality, agentic task completion) earns citations on evaluation-specific queries. The write-up should name the framework, explain the methodology, share benchmark data, and be linked to a Papers with Code entry where the code is reproducible.

OSS tooling READMEs on active repositories. Active repositories with recent commits, structured READMEs, and deployment documentation are indexed regularly by Perplexity. Inactive repositories with stale README content are not. The README is a technical essay: problem statement, use context, deployment proof, named authors.

Hugging Face model cards on deployed or fine-tuned models. If the firm ships fine-tuned models or deploys public model evaluations, a Hugging Face model card is a citation anchor. The card needs eval data, vertical specificity, and named authorship to be retrieved.

“What we do not build” pages. Boundary statements outperform capability lists for AI consultancies AI visibility. A page explicitly stating which use cases the firm declines (“we do not build general chatbots, we do not take on projects without a defined eval methodology”) retrieves better for the use cases the firm does claim. AI assistants pull from pages that are specific and bounded, not pages that are comprehensive and generic.

Vertical solution pages with named methodologies. A solution page for “RAG for legal document review” that explains the firm’s specific approach, names the methodology, cites the benchmark performance, and credits the named practitioners who designed it. The page states the specific system the firm builds and the evidence it works, not just “we offer RAG for legal.”

Partner and practitioner bio pages with full citation history. Individual practitioner pages linked from GitHub, dev.to, LinkedIn, and conference profiles. Each bio page should list: the practitioner’s specific use-case expertise, their production deployments with firm-approved disclosure, their technical writing with direct links, their conference talks with linked speaker pages, and their OSS contributions. This is the entity anchor page that AI assistants cross-reference when verifying practitioner credentials.

Structured benchmark reports under firm name. An annual or semi-annual benchmark on a specific narrow problem earns press placements, creates a named entity (the benchmark itself becomes citable), and builds training-corpus presence. The benchmark needs a named lead researcher, a reproducible methodology, and publicly available data. “The 100 AI Consultancy RAG Benchmark 2026” is a fictional but illustrative example of the format.

A 90-day AI visibility plan for AI consultancies

The plan is sequenced by dependency. The citation footprint audit comes first because it tells you which queries matter and which surfaces need fixing. Named-practitioner asset gaps come second because they are the highest-leverage input. Content creation comes third because publishing without the entity infrastructure in place reduces the compounding effect.

Days 1-14: Citation footprint audit across 30-50 queries. Run the queries your buyers actually use: “best AI consultancy for [your use case] in [your vertical],” “who builds [your specific system] for [your specific buyer type],” “[your methodology] consulting,” and variations of each across ChatGPT, Perplexity, Claude, and Google AI Overviews. Document every firm that appears, which sources get cited, and which of those sources you have equivalents for. This audit reveals the retrieval pool you are competing against and the surfaces you are absent from.

Days 1-21: Named-practitioner asset gaps fixed. Before publishing new content, ensure the infrastructure for practitioner citation is correct. Each named practitioner should have: a GitHub profile with a consistent bio and linked publications, a dev.to or Medium author page with at least one published essay, a LinkedIn profile with use-case-specific positioning that matches the website, and a practitioner bio page on the firm’s website with full citation history. Inconsistent naming across surfaces (“J. Chen” on GitHub, “Jane Chen, PhD” on LinkedIn, “Jennifer Chen” on the website) dilutes entity recognition. Standardize everything.

Days 15-45: Publish 2-3 named-practitioner production write-ups plus 1 eval report. The first production write-up should be on the firm’s most defensible use case, authored by the most credentialed practitioner, with the most specific deployment data available. Format: problem statement, architectural decisions with reasoning, evaluation methodology and results, production outcomes, what failed and what the team would do differently. Length: 2,000-3,500 words. Published on the firm’s technical blog and cross-posted to dev.to with the practitioner’s byline.

The eval report should introduce or document a specific evaluation methodology with named benchmarks and public data. Submitted to Papers with Code. Linked from the firm’s website.

Days 21-45: Hugging Face model cards and Papers with Code entries optimized. For firms with deployed models or public evaluations, publish or update Hugging Face model cards with production context, vertical specificity, eval data, and named authors. Submit eval frameworks to Papers with Code with reproducible code. These are live-retrieval citation anchors that appear within weeks of publication for relevant queries.

Days 30-60: 3-5 conference speaker placements with retrievable bio pages. Submit practitioners to AI Engineer Summit, MLOps Community Conf, PyData city-level events, and any upcoming NeurIPS or ICML workshops with practitioner tracks. For events in the next 30 days, prioritize submission speed. For events 60-90 days out, prepare a strong proposal with use-case specificity. After each accepted talk, insist on a speaker bio page with 200+ words and linked technical assets. The page URL is the citation anchor.

Days 45-75: Independent expert-citation pages and OSS tooling launch or update. Identify 2-3 independent expert-citation surfaces beyond conference pages: a G2 profile with use-case-specific description, a Built In company page, a relevant AI directory listing (AI Multiple, StackShare for the technical stack). Each should carry the same practitioner names and use-case positioning with identical entity language.

For OSS: if the firm has a relevant open-source tool, update the README to citation-eligible format (prose explanation, production context, named authors). If no OSS exists, evaluate whether a small tool (evaluation harness, RAG helper, dataset preprocessor) would support the core use-case positioning and is deliverable in 30 days.

Days 1-90: Weekly query monitoring. Run the 30-50 baseline query set weekly across all platforms. Track: which firms appear, which sources get cited, whether your firm appears, and which practitioners are cited by name. The pattern that signals momentum is when your firm’s practitioner essays, GitHub repositories, or conference pages start appearing in the cited source list, even before your firm appears in the answer text. Source citations precede answer appearances by 2-4 weeks typically.

Day 90: Re-audit and adjust. Repeat the full citation footprint audit from Day 1. Compare current citation appearances against the baseline. Identify which use-case queries moved, which surfaces drove the citations, and which queries remain unaddressed. This re-audit determines whether the next 90 days should continue the current surface mix, add training-corpus investments (arXiv preprints, tier-1 technical publications), or shift use-case focus based on where citation traction was strongest.

How to choose an AI visibility partner for AI consultancies

Most marketing agencies claiming AI visibility expertise for B2B services firms are repurposing generic SEO or thought-leadership playbooks. For AI consultancies specifically, the gaps in generic advice are consistent.

HBR and tier-1 business publication placement gets recommended as the primary pathway. For engineer-founder AI shops, that is the wrong center of gravity.

Most cannot explain what makes a GitHub README a citation surface, which Hugging Face presence patterns matter, or how Papers with Code entries function in retrieval pools for technical queries.

AI visibility gets measured by head-term citation (“does your firm appear when we search best AI consultancy”) rather than use-case citation (“does your firm appear when a buyer asks about your specific deployment context”). Head-term measurement is irrelevant for a boutique with no brand recognition. Use-case measurement is where boutiques win.

Named-practitioner entity consistency across technical surfaces goes unaddressed. Most agencies can audit a website. Few can audit the cross-surface entity coherence of a technical practitioner’s identity across GitHub, dev.to, LinkedIn, Hugging Face, and conference pages.

Questions worth asking any AI visibility partner for your AI consultancy:

Can you explain the difference in citation patterns between Perplexity and Claude for AI-services queries? A partner who cannot explain this does not understand the retrieval surfaces that matter for AI consultancies.

Have you worked with technical content surfaces? GitHub README optimization, Hugging Face model card structure, and Papers with Code submission are not standard SEO skills. Ask for examples.

What does your 90-day measurement setup look like? If the answer is a dashboard showing Google rankings, the partner is not optimizing for AI citation. If the answer is a query monitoring pool of 30-50 use-case queries across ChatGPT, Perplexity, Claude, and Google AI Overviews with weekly tracking, they understand the measurement.

How do you approach named-practitioner entity consistency? If they have not thought about this as a distinct deliverable, they are solving for firm-level optimization and missing the practitioner-level signal that dominates AI consultancies citation.

See the ranked list of demand generation agencies for AI consultancies for vetted partner options.

Key terms

Named-practitioner content. Technical writing authored by a specific individual (not by the firm) with a linked GitHub profile, conference speaker history, and verifiable credentials. AI assistants cross-reference practitioner claims against external sources. Named-practitioner content is cited at 3.2x the rate of anonymously or corporately authored content for AI consultancy queries.

Entity consistency. Matching naming conventions, use-case positioning, and affiliation language across GitHub, LinkedIn, Hugging Face, dev.to, conference speaker pages, and the firm’s website. Inconsistency between surfaces dilutes AI entity recognition. A practitioner described as “RAG specialist” on GitHub and “AI transformation leader” on LinkedIn does not resolve as a single authoritative entity for a specific query.

Training corpus presence. Content absorbed into an AI model’s weights during pre-training. For AI consultancies, strongest from arXiv preprints, NeurIPS and ICML proceedings, and well-indexed technical writing at time of model training. Slow to build; high authority once established.

Live retrieval. Real-time web search used by Perplexity, Claude with search enabled, and ChatGPT’s browse mode. For AI consultancy use-case queries, live retrieval is the dominant mechanism. Recency matters: content updated within 30 days receives materially higher Perplexity citation rates than older content.

Citation footprint audit. A systematic test of 30-50 use-case queries across AI platforms to determine which firms appear in responses, which sources get cited, and where your firm is absent. The input to any AI visibility plan. Without this audit, there is no baseline and no way to measure progress.

Eval framework. A defined methodology for measuring AI system performance on a specific task (retrieval accuracy, classification precision, task completion rate). Named eval frameworks published with public benchmark data earn independent citations on evaluation-specific queries and function as training-corpus anchors.

Model card. Structured documentation for a machine learning model, published on Hugging Face or similar platforms, that describes the model’s purpose, training data, evaluation results, intended use, and known limitations. Named-author model cards with production context are citation-eligible surfaces for AI consultancy queries on Perplexity.

6-citation threshold. ExaltGrowth’s 2026 cross-vertical finding: brands with 6 or more citations across an AI platform’s retrieval pool are 6x more likely to appear in recommendations for relevant queries than brands with 1-5 citations. For niche use-case queries, this threshold is achievable in 90 days for a firm with active practitioner content. For head terms, it is an 18-month investment.

How 100Signals approaches AI visibility for AI consultancies

AI visibility for AI consultancies is integrated into every deliverable because the citation surfaces for this ICP (GitHub, Hugging Face, dev.to, conference speaker pages, eval reports) require a coordinated publishing and entity-building system, not a standalone optimization pass.

Authority at $3,500/mo/mo for 3 months builds the named-practitioner content and AI search visibility infrastructure: the citation footprint audit, the entity consistency fix across technical surfaces, 2-3 production write-ups with named authorship, the eval report and Papers with Code submission, the Hugging Face model card structure, and the conference speaker placement submissions. It also covers the weekly query monitoring that tells you which surfaces are driving citation traction and which need additional investment.

System at $7,000/mo/mo for 3-5 months adds the trigger-event monitoring and outbound layer. AI consultancy buying triggers are among the most observable of any ICP: model deprecations, AI Act compliance deadlines, funding rounds with AI roadmaps, production AI failures, new CTO or Head of AI hires. System layers intent monitoring across these triggers against a named target account list, adds LinkedIn and outbound sequences timed to those triggers, and adds the editorial placements (Towards Data Science, The Gradient, adjacent publications) that build training-corpus presence and entity authority in parallel with the live-retrieval work.

Both engagements are run async with weekly reporting. Everything built during the engagement belongs to the client.

The firms that get cited run a coordinated system where named-practitioner content, technical surface presence, and trigger-monitored outbound reinforce the same use-case positioning. AI citation is the compound result.

See how it works.

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Related: Lead generation for AI consultancies | Demand generation for AI consultancies | Best lead generation companies for AI consultancies | Best demand generation agencies for AI consultancies | AI visibility for consulting firms