ServiceStrong
A power procurement advisory for mid-market energy buyers during the NextEra-Dominion review
A focused advisory service that helps commercial and industrial energy buyers in Virginia, North Carolina, and South Carolina understand what the merger's 180-day regulatory review means for their existing contracts and forward procurement decisions. Delivered as a monthly briefing plus on-call access to a regulatory analyst.
- Why now
- The regulatory clock is already running. Large buyers are reading everything they can find on this merger, which means the question is live and no clean answer exists yet. A 180-day review window is exactly the right length for a structured advisory engagement.
- Who
- Energy brokers, independent power consultants, and commercial real estate operators with significant utility exposure in the affected states.
- First move
- Pull the FERC filing and Virginia SCC docket this week, map the five or six decision points that matter most for large buyers, and draft a one-page summary you can send to ten existing clients as a proof of concept.
Signal nextera, dominion
ServiceStrong
An AI content audit that shows B2B companies whether they appear in AI-generated answers
A one-time audit, with an optional monthly tracking subscription, that tests whether a company's brand and products surface in the AI-generated answers that early-stage B2B buyers are getting from tools like ChatGPT, Perplexity, and Google's AI overviews. The output is a gap report with specific content fixes ranked by likely impact.
- Why now
- B2B enterprises are discovering they rank well in traditional search and are invisible in AI answers. That is a new and specific problem, and readers are treating it as urgent. No clear category leader has emerged yet for mid-market companies.
- Who
- B2B content agencies, SEO firms expanding their service line, and MarTech consultants who already have client relationships in the space.
- First move
- Run the audit manually for one existing client this week using five or six of their target buyer queries across three AI tools. Document the gap and the fix list. That is your proof of concept and your sales tool.
Signal ai tools, ai adoption, b2b marketing
ServiceStrong
A fixed-price AI governance gap assessment for mid-market enterprises
A structured four-to-six week engagement that maps what AI tools a company is actually running, what governance exists around them, and where the gaps are. Delivered as a written report with a prioritized remediation list. Priced as a flat engagement, not hourly.
- Why now
- The Smarsh study found that only 26% of enterprises have governance keeping pace with deployment, and 55% are already deploying. The MIT finding that only 11% have deep integration suggests most companies are somewhere in the messy middle: using AI but not managing it. That is exactly where a structured assessment sells.
- Who
- Management consultants, IT advisory firms, and compliance-focused professional services firms that already have mid-market enterprise relationships.
- First move
- Write a two-page scope document this week that defines what the assessment covers, what the deliverable looks like, and what it costs. Price it based on company size, not hours. Send it to three existing clients as a named offering.
Signal ai governance, enterprise ai, ai integration
ProductBuilding
A pre-built AI implementation package for midmarket B2B companies under $3 billion in revenue
A bundled implementation product that takes a midmarket company from 'we bought the tools' to 'the tools are running workflows' in 90 days. Built on top of existing platforms like Google Cloud Gemini or similar, with templates for the three or four use cases that actually move metrics in B2B operations: lead qualification, content production, customer support triage, and internal knowledge retrieval.
- Why now
- Accenture and Google are targeting exactly this gap with Accenture Edge. The demand signal is confirmed, but their product is enterprise-grade. The midmarket version, faster and cheaper, is not yet owned by anyone obvious.
- Who
- Boutique technology consultancies and implementation partners already working with companies in the $50 million to $3 billion revenue range.
- First move
- Pick one use case, lead qualification or content production, and build a working 30-day implementation guide using one platform this week. Price it as a product with a fixed cost, not a consulting engagement.
Signal ai tools, ai adoption, enterprise ai
ContentBuilding
A regulatory and construction startup deal tracker for construction industry operators
A weekly briefing and database that tracks Y Combinator and other early-stage construction tech startups, maps them to the operational problems they claim to solve, and pairs them with relevant federal initiatives like the DOE Advanced Building Construction program. Sold to general contractors, developers, and real estate operators as a scouting service.
- Why now
- A 44-company YC cohort just hit the market focused on construction bottlenecks, and the DOE is pushing money into the same problems. Construction operators are reading about both, which means there is active demand for someone to synthesize it.
- Who
- Construction technology consultants, industry associations, and general contractors with innovation or technology teams.
- First move
- Compile the 44 YC construction cohort companies into a one-page matrix this week, mapped to problem type. Send it to five general contractor contacts as a free sample and ask what they would pay for it monthly.
Signal construction industry, startups
ProductBuilding
An agentic AI spend management tool for enterprise teams already over budget
A lightweight SaaS dashboard that tracks agentic AI workflow costs in near-real time, flags when response refinement costs are spiking, and surfaces which workflows are consuming budget without clear output. Integrates with the major AI platforms via API.
- Why now
- McKinsey data cited in current reading shows 93% of enterprise AI teams are over budget, and 60% of agentic AI cost goes to response refinement. That is a specific, measurable problem. No obvious cost management tool exists for this layer yet.
- Who
- Enterprise software companies with existing integrations into AI platforms, or fintech companies that already build spend visibility tools for SaaS.
- First move
- Interview five enterprise AI team leads this week. Ask them one question: how do you currently know when an agentic workflow is costing more than it should? The answer, or the absence of one, is your product specification.
Signal enterprise ai, ai integration, ai governance
ServiceBuilding
A forward-deployed AI implementation team as a standalone professional services firm
A small firm, ten to twenty engineers and operators, that embeds inside one client at a time for six to twelve months to build and run AI systems on-site. Modeled on the Anthropic Ode and Microsoft Frontier Company approach, but sized and priced for companies between $100 million and $2 billion in revenue that cannot get attention from those programs.
- Why now
- Anthropic and Microsoft just told the market, with $4 billion in combined capital, that embedded deployment is the model that works. The enterprises they are targeting are very large. The companies just below that threshold have the same problem and no one is coming for them.
- Who
- Experienced AI engineers or product managers leaving large tech or consulting firms who want to build an independent practice. Also a strong acquisition target for mid-market IT services firms looking to move up the value chain.
- First move
- Define the engagement model this week: one client at a time, minimum six-month term, specific deliverable at 90 days, clear exit criteria. Price it as a monthly rate, not a project cost. That document is your founding thesis.
Signal anthropic, ai integration, enterprise ai
ProductEmerging
A power load forecasting product for data center developers tied to utility merger timelines
A data product that helps data center developers and hyperscaler real estate teams model how utility mergers, regulatory reviews, and grid interconnection queues will affect available power capacity and pricing in specific markets over a three-to-five year horizon. Delivered as a structured dataset with a quarterly update.
- Why now
- The NextEra-Dominion merger is explicitly being read as an AI data center load growth story. Data center developers need to make site decisions years ahead of when power is available. The merger creates new uncertainty in four states that are among the most active data center markets in the country.
- Who
- Data center site selection consultants, hyperscaler real estate teams, and energy advisory firms that already work with large power buyers.
- First move
- Build a one-page model this week that shows how the merger's regulatory timeline maps to interconnection queue timelines in Virginia. That is the core product. Send it to three data center contacts and ask whether they would buy a quarterly version.
Signal nextera, dominion
