Four ways we work with established small and mid-sized businesses. Engineering ships the system. Advisory makes sure your team uses it. Most engagements blend two or three. On the first call we'll tell you which fit - and refer you out if none of them do.
Custom builds for the work your team actually does, not the deck version. Dashboards, websites, internal tools, backends, integrations - the whole stack.
Sometimes the right answer is custom software, not AI. We design and ship internal tools, customer-facing product features, and the backend systems they sit on top of. The decision is always pragmatic: pick the right altitude for the problem.
Default stack: TypeScript or Python, Next.js or Astro, Postgres, Vercel/Cloudflare/AWS - integrated with your existing systems via API or webhooks. Code lives in your GitHub. Deploys land in your cloud. You own everything we build, outright.
When AI belongs in the build, we integrate it where it earns its place - structured extraction from documents, embedded copilots inside your product, customer-facing features. Otherwise we leave it out. A 30-line script that saves a department twelve hours a week is a better outcome than a fashionable AI rewrite.
What this can include
Drop-in AI for systems your team already uses. Search, copilots, classification, summarization, retrieval, agentic workflows. Connects to your existing data, UI, and access controls. Eval-first so it keeps working under real load.
Three things tend to be the heart of an AI integration engagement: retrieval over your private documents, agentic workflows that run in production, and - when data can't leave your network - self-hosted inference infrastructure.
For retrieval, we build RAG pipelines that actually answer questions about your business, not the public web. Document chunking strategies tuned to your content, hybrid lexical-plus-dense retrieval, re-ranking, and citation back to source so every answer is verifiable.
For agentic systems, we build multi-agent runtimes with structured-output pipelines, typed MCP workflows, eval harnesses, and human-in-the-loop checkpoints. We default to languages your team already knows (Python or TypeScript), deploy in your cloud, and hand over code we'd be proud to maintain at 3 a.m.
For workloads where data can't leave your network - SEC, HIPAA, attorney-client, classified - we deploy local inference stacks (vLLM, Ollama, TGI) on your hardware or VPC. Quantization, KV-cache management, batched routing across mixed model sizes. Same eval discipline. Zero data egress.
What this can include
Agents that handle the paperwork your team is sick of doing. Invoices, RFQs, intake, document triage, meeting notes - the small repetitive decisions that eat your team's day.
Most teams have a list of recurring tasks that aren't quite worth a human's attention but aren't quite simple enough for off-the-shelf tools. That's where automations land best - the gap between "spreadsheets and email" and "we should hire someone."
We build multi-step agent workflows that wire into your existing systems: documents come in, get parsed and routed, decisions get made (or escalated), and the audit trail lands in a place your compliance team can review. Always with a human in the loop on the calls that matter.
We measure baseline before we change anything. The before-numbers (hours per week, error rate, turnaround time) are how we know whether the automation actually moved anything. Adoption-theater dashboards don't count.
What this can include
From AI curiosity to confident daily use - plus senior strategic partnership when you need it. No hype slides. No all-hands offsite trainings. We sit with the people doing the work, find the spots where AI saves real time, and stay until the new way of working takes.
Four ways to engage on the advisory side: fractional GTM leadership, applied AI for GTM teams, executive AI coaching, and team office hours. Most engagements blend two or three.
The same four-step rhythm runs through every advisory engagement: listen, map, pilot, measure. Two 30-minute conversations per person involved. A prioritized list of leverage points - usually 8 to 15 - scored by hours saved per week. Hands-on sessions on the team's actual work. Then 30 days later, we re-measure. What stuck, what didn't, why.
We do not run weeklong workshops, build a Notion wiki of "AI prompts," or sell licenses for tools you already pay for. Adults learn AI fastest by using it on their actual work, not in a sandbox.
Advisory rhythm
/04.01 fractional
Fractional CMO or head of GTM. We sit in your leadership meetings, set quarterly priorities, and roll up our sleeves on the work that needs senior judgment - positioning, pricing, channel strategy, sales enablement, hiring the right next person. Bring the strategic thinking; leave with the operating system to run it.
/04.02 applied ai
For sales, marketing, finance, and ops teams. Workflow interviews with each person, a build-vs-buy matrix scored by impact and feasibility, hands-on sessions on the team's actual work, written playbooks the team can re-run without us, and 30-day adoption measurement.
/04.03 executive coaching
One-on-one for founders, CEOs, and senior leaders. Bi-weekly 60-minute confidential sessions. Personal AI workflow setup, competitive intelligence on what your peers are shipping, sounding-board for hire-vs-build decisions. Three-month minimum, then month-to-month.
/04.04 office hours
Weekly or bi-weekly drop-in slot. Open booking calendar - anyone on the team can grab time. Async problem channel between sessions (Slack or Loom). Quarterly summary of what came up, with patterns to share. Optional escalation to engineering when a custom tool is the answer. Month-to-month, cancel anytime.
Get found in the AI-era discovery layer. Answer engines, generative search, classic SERPs. AEO, GEO, and SEO - three overlapping disciplines that decide whether your customers find you in 2026.
Discovery has fragmented. Your customers find you through Google AI Overviews, ChatGPT and Perplexity answers, Bing Copilot, classic SERPs, and emerging generative search engines - each with its own retrieval logic and citation behavior. Optimizing for "search" today means optimizing for three overlapping disciplines at once.
We make your content extractable, citable, and authoritative across all three layers. The output is structured, evidence-rich content that AI assistants will quote and search engines will rank, plus the technical foundation - schema, entity graphs, internal linking - that makes it discoverable. Engagements include a full visibility audit, schema and content rebuilds, an authority-signals plan, and weekly reporting on citation rate and AI-overview inclusions.
We don't sell "SEO content packages" or game ranking algorithms. We do the structural and editorial work that holds up under both classic search ranking and AI-engine synthesis - work that makes your business more discoverable for the next decade, not the next quarter.
What this can include
/05.01 aeo
Structuring your content so AI assistants (ChatGPT, Perplexity, Claude, Gemini) can extract and cite it accurately. Schema, entity clarity, source authority, evidence chains, citation-friendly content shape.
/05.02 geo
Optimizing for generative search results - Google AI Overviews, Bing Copilot, SearchGPT. Content shape, factual density, and authority signals that survive synthesis.
/05.03 seo
Classic on-page SEO done right for the AI era - semantic targeting, intent matching, structured data, internal linking, and content hubs that build topical authority.
The first call is free. We'll tell you in week one whether we're the right fit - and refer you out if we're not.