A lean, AI-augmented engineering team retained on a monthly basis, working on your roadmap as an extension of your own org. AI fluency is table stakes, and the craft is held to the bar you would hire against in-house. We cover the Claude Team subscription each engineer uses on your work, you interview each engineer before the team is finalized, and time off is not billed.
Groundlight 
GloFlow 
Asana Rebel 
ElephantCPA 
DocBraces 
Notary 
Plannerd WHAT THIS ENGAGEMENT LOOKS LIKE
The outcome is the roadmap moving every week without dropping the engineering bar you would have hired against in-house. The team is the means. The service around it, the engineer selection that clears our bar before yours, the senior partner who stays in the engagement, and the working model that drops the team into your rituals on day one, is the part most prospects underestimate.
A small full-time team built for speed and craft, not bench depth. Every engineer ships AI-augmented by default, so a three-person team here moves at the pace of a larger one that has not adopted modern AI tooling, without trading the engineering bar that would clear an in-house hire. Compositions range from three engineers, to three engineers with a delivery manager when you do not have internal engineering management for an embedded team, to five-to-eight engineer setups with senior engineering leadership inside the team for larger engagements.
The team works inside your stack and your cadence: your stand-ups, your planning rhythm, your code review process, your Slack, your Linear or Jira, your on-call rotation. The working model is locked in the proposal before kickoff, so there is no multi-week integration ramp on either side. By week one the team is shipping against your roadmap, not still finding the meeting invites.
Leanware senior people stay in the engagement: delivery management, technical direction when you ask for it, and the partner who scoped the team is the same one who signs off when the composition needs to change. Most of our clients stay with us from early inception through acquisition, scale, or sunset. We flex team composition with the stage instead of churning the relationship.
HOW IT WORKS
The engagement starts with a 30-minute discovery call. Someone who understands both the business and the engineering walks through your roadmap, the capacity gap you are filling, and your runway, and decides whether this is the right line or whether a milestone-billed AI Product Engineering build is a cleaner fit. You are talking to a senior technical person who also gets the commercial side, and the routing decision is made before any team-scoping conversation starts.
If the call clears, we move into team-composition scoping (number of engineers, mix of seniorities, delivery manager included or not), then into engineer selection against the role and your context. You interview each candidate before the team is finalized. Kickoff lands two to four weeks from signing, with the team fully staffed and integrated into your rituals on day one. There is no separate setup fee. The work to stand the team up is folded into the first month of the retainer.
Someone who understands the business and can build the solution. We walk through your roadmap and the capacity gap, and decide whether the engagement is Dedicated Teams or a milestone-billed AI Product Engineering build. The call is 30 minutes.
Team size, mix of seniorities, delivery manager or not, working model, target start date. The proposal commits to a precise composition and a monthly retainer against it.
Each engineer clears Leanware’s internal hiring process first (technical assessment, multiple interview rounds, final interview with C-level). You interview every finalist before the team is locked.
The team integrates with your tools and rituals on day one. Replacement, buyout, and working-model terms are set in the master engagement agreement. From here it is a sustained partnership, not a transaction.
HOW WE KEEP IT STABLE
Most dedicated-team services in the market are sourcing layers: they recruit, place, and bill, but the engineering bar is whatever you are willing to accept and the replacement decision happens without you in the room. The four mechanics below are why this engagement holds where staff augmentation degrades.
Every engineer placed on a Dedicated AI Engineering Teams engagement clears the same hiring process Leanware uses for its internal builds: technical assessment, multiple interview rounds, culture fit, and a final interview with our C-level. Once the engineer clears our gate, you interview them. Your interview is a fit check on top of an engineering check that already passed. A Toptal or Andela engineer can be excellent, but the bar there is set by marketplace volume incentives, not by an engineering firm hiring the engineer for its own work.
When an engineer is not the right match for either side, we run a replacement process. We surface the issue early, work through whether it is a coaching problem or a fit problem, and replace the engineer when replacement is the right answer. You do not bear the cost of a bad placement, and a replacement engineer is not placed on your team without you in the interview loop.
Engineers who fit your team can convert to in-house hires under a structured buyout. The terms scale with how long the engineer has been on the engagement: longer engagements carry lower buyout fees because the relationship has paid back. This is part of why the line works for teams who need sustained capacity now and may want to convert some engineers to in-house roles after the next round closes.
Every engineer placed on this line is fluent in modern AI tooling, AI-augmented development workflows, LLM APIs, and agent patterns. There is no "regular team" and "AI team" split, and AI fluency is not priced as an upcharge. Leanware covers the Claude Team subscription each engineer uses on your engagement, so the AI tooling cost does not show up as a separate line on your invoice.
PRICING
Each engagement is its own configuration of team size, seniority mix, and delivery manager inclusion. The retainer is set against that configuration during the discovery call. The sample compositions below give you a sense of the shapes the line carries; the concrete number comes together once we agree on the team.
These are sample compositions, not specific past customers. Time off is not billed. The Claude Team subscription each engineer uses on your engagement is covered. Specific buyout fees are set in the master engagement agreement.
Series A or Series B CTO racing hiring
The line’s modal engagement. A Series A or B startup CTO extends platform or product capacity by three engineers while the internal hiring pipeline catches up. The client runs the team day-to-day. Typical run length is twelve months and longer.
Seed-stage founder, non-technical or solo technical
A seed-stage founder shipping faster than they can hire, often without a CTO. The delivery manager covers the engineering-management role inside the team so the founder is not running an embedded team without the time to direct it well.
Mid-market head of engineering extending a surface
A $20M to $200M revenue company extending vertical-product or platform capacity. The most quality-sensitive segment in the line. Senior engineering leadership inside the team carries technical direction with the client’s VP Eng or head of engineering.
WHAT A PROPOSAL CARRIES
There are no named tiers. Each proposal commits to a precise configuration along these axes.
IS THIS YOU
If one of these describes your quarter, the engagement is sized for you. If none of them does, the discovery call will tell us both and route you elsewhere directly.
You run an engineering org of 8 to 25 engineers, a roadmap commitment is slipping, and the hiring pipeline is not closing fast enough. You need to extend capacity by three to six engineers while hiring catches up. You have been burned by staff augmentation placements that did not hold up, and the engineer-selection bar matters to you more than the cost per hour. The line’s strongest segment.
You are pre-Series A, often a non-technical founder or a technical founder without a CTO. You need capacity now and the roadmap is evolving fast enough that a fixed-scope engagement does not fit. You want a senior counterpart you can talk to, not a marketplace. The delivery-manager inclusion in the team is usually the right shape because running an embedded team without the time to direct it well is the failure mode for this segment.
You are CTO, VP Eng, or head of engineering at a $20M to $200M revenue company. A specific surface (a vertical product, a platform team, a customer-facing app) needs sustained capacity that does not justify permanent in-house hires yet. You want stability, engineering practice, and clean integration with your existing org. The engineer-selection bar and the AI-fluency baseline are what brought you to this page.
You need engineering capacity now and are also evaluating Leanware engineers as potential in-house hires when the next round closes. The structured buyout is part of what you are paying for. Engineers who fit your team convert to in-house roles cleanly; the engagement structure does not push you toward either path.
HOW THIS IS DIFFERENT
Four alternatives cover most of the market for sustained engineering capacity in the funded-startup and mid-market band. Each wins on a specific shape of need. The Dedicated AI Engineering Teams line is built for teams who want sustained capacity from a partner that hires engineers to the same standard the firm uses for its own builds and stays in the engagement past the placement.
| Comparison | Dedicated AI Engineering Teams | Staff-aug marketplaces | Nearshore agencies | In-house hiring | Large consulting firms |
|---|---|---|---|---|---|
| Engineer selection bar | Same hiring process Leanware uses for its internal builds. Final interview with our C-level. You then interview every finalist. | Marketplace bar, set by volume incentives. | Variable. Often drifts with turnover. | Your bar, but the pipeline takes months to close roles at it. | Partner ladder. Associates do the work. |
| Time to fully staffed | 2 to 4 weeks from signing. | Days to weeks. You vet each individual. | 4 to 8 weeks. Variable by agency. | 3 to 9 months per role at the bar you want. | 8 to 16 weeks. Procurement-led. |
| Engagement stability | Senior partner stays in the engagement. Replacements involve you. AI fluency baseline does not drift. | Engineer-level relationship. No partner layer if someone churns. | Often degrades 12 to 18 months in: turnover, replacement bar drifts. | High when retention holds. Vulnerable to single-point-of-failure exits. | Stable team brand. People rotate. |
| AI fluency | Baseline for every engineer. Claude Team subscription covered. | Per-individual. You filter for it. | Variable. Often retrofit, not baseline. | Whatever you hire for. | Practice-level. Specialist pricing. |
| Pricing shape | Monthly retainer, scoped to your team composition. No setup fee. Time off not billed. | Hourly markup per engineer. Time tracked. | Hourly or monthly. Cost-led positioning. | Salary + benefits + recruiter fees + ramp. | Monthly at scale. Engagement minimums above this segment. |
| Best fit | Funded startups and mid-market teams wanting sustained capacity with a partner. | Short-term individual placements where you run the bar. | Cost-led extensions where price beats stability. | Roles you can hire for inside your runway. | Fortune 500 engagements at scale. |
| Where it loses | Defined-scope builds dressed as a team retainer (route to AI Product Engineering). Sub-three-engineer engagements. Enterprise procurement. | Sustained team stability. Partnership layer. | Quality after 12 to 18 months. | Time-to-capacity when hiring is slow. | Sub-$200K engagement sizes. Speed. |
Two things make this engagement different in kind. The same firm that selects the engineers also stays in the engagement through delivery management, technical direction, and replacement decisions, so the engineering bar holds at month 18 instead of drifting after the first turnover. And AI fluency is built into the engineer-selection bar rather than priced as a premium tier, so the team you hire today is the team that ships AI features in your roadmap next quarter without a renegotiation.
SELECTED WORK
Dedicated AI Engineering Teams engagements are multi-quarter relationships, so the strongest evidence is the engagements that have run for a year or longer. Groundlight is the line’s featured anchor: an ongoing engineering-capacity engagement with a US-based AI computer vision SaaS, structured as a stable team retained on a monthly basis. The other cases below show the shape across geographies and technical domains.
A dedicated-team backend and AI engagement supporting Connect Capable's traffic incident management platform. Work covers OpenAI-powered incident correlation, data pipeline refactoring, and the Azure-side infrastructure that runs it.
Dedicated AI Engineering Teams Read case studyA dedicated-team engagement that pairs a senior data engineer and a tech lead with Greyhound Engineering's existing analytics team. The work centers on Grafana-based dashboards integrated with SQL Server and the N3uron industrial-data API.
Dedicated AI Engineering Teams Read case studyA dedicated-team staff-augmentation engagement supporting ENOVIS, a software consultancy in Aruba, on a custom workforce-management platform for their janitorial-services client. Project, staff, and cost tracking shipped as a single web application.
Dedicated AI Engineering Teams Read case studyCLIENT VOICE
We started this process looking for an outsourcing partner and feel like we've ended up with so much more. They take ownership of everything, and their senior developers are always around to support us. We trust their judgment because they are extremely reliable.
Morgan Venable
Head of Product, Groundlight
FREQUENTLY ASKED
Most of these come up before the team-composition scoping conversation. The answers below match what actually happens once the engagement starts.
We walk through your roadmap and the capacity gap, decide whether the engagement is Dedicated Teams or a milestone-billed AI Product Engineering build, and start the team-composition scoping conversation when fit holds. If it is not a fit for us, we say so directly.
Tell us about your roadmap and the capacity gap you are filling. Someone who understands the business and the engineering will walk through it with you, decide whether the engagement is Dedicated Teams or a milestone-billed AI Product Engineering build, and start the team-composition scoping conversation when fit holds. If it is not a fit for us, we say so directly.