Forward-deployed AI consultancy
We put AI-native engineers inside your team — your Slack, your systems, your real workflows — to ship production AI. Not pilots. Not decks.
What we do
We embed. We ship. We measure in hours returned.
We embed AI-native engineers directly inside operationally complex businesses to design, build, and ship AI systems wired into your real infrastructure — your data, your tools, your security boundaries, your day-to-day workflows.
It's the forward-deployed engineering model, applied to the large language model era. We sell outcomes, not hours, and we measure success in workflows automated and hours returned to the business — not slide decks delivered.
Why now
The bottleneck isn't the model. It's integration.
Most companies already have access to frontier models. Very few have wired that access into measurable operational impact — and that's where the work actually is.
Foundation models have crossed the capability threshold for real operational tasks — not just content generation.
Operations leaders are under pressure to show measurable AI ROI in 2026, not just AI adoption.
Internal AI teams at most mid-market companies are thin or nonexistent — a built-in build-vs-buy gap for embedded talent.
The forward-deployed model is proven at scale, but until now, largely out of reach for mid-market companies.
How we work
Four things that don't change between engagements.
Embedded, not advisory
We work inside your tools — Slack, internal systems, ticketing — not from a deck presented at the end of the month.
Built for speed
You'll see a shippable workflow improvement within 2–4 weeks of kickoff, not a six-month roadmap.
Outcome-aligned pricing
Once we've proven impact together, pricing can shift to reflect outcomes delivered, not just hours billed.
Narrow, deliberate focus
We work in operationally complex, data-rich industries — where embedded engineering has the most leverage.
Engagement model
An engagement built like a ladder.
Each stage builds trust and surfaces the next opportunity — you can stop at any rung.
Discovery Sprint
1–2 weeksEmbedded mapping of your workflows, data sources, and the highest-leverage AI use cases.
Embedded FDC Engagement
4–12 weeksOne consultant embedded on-site or in-Slack, building and shipping one production workflow.
Pod Engagement
8–24 weeksA 2–4 person embedded team covering multiple workflows, plus the infrastructure to support them.
AI Ops Retainer
OngoingMonitoring, maintenance, and iteration of everything we've shipped, after launch.
Who we're for
Built for operationally complex, mid-market teams.
Firmographics
- $50M–$5B in annual revenue — enough operational complexity and budget, without an in-house AI build team
- Operationally heavy, data-rich industries, where the integration work is hardest and the leverage is highest
- Already using AI at the individual level — ChatGPT seats, for example — but not yet wired into core workflows
Who we work with
- COO, Head of Operations, VP Engineering, or CTO — someone who feels the operational pain directly
- A buyer who owns budget and a mandate to ship into production, not just evaluate
- Not a fit for pure R&D or "innovation team" budgets without authority to deploy
Get in touch
Let's wire AI into how you actually work.
Tell us about the workflow that's costing you the most hours. We'll tell you, honestly, whether we're the right fit to fix it.
sales@inferants.com