Enterprise AI staff augmentation services built around your outcome
Senior AI engineers embedded inside your sprints. First PR merged in 11 days. You own the intellectual property, the codebase, and the outcome. We handle onboarding, governance, and delivery.
Why most AI hiring approaches stall your roadmap
The standard path to building AI capability trades six months of overhead for one engineer. Your roadmap does not survive that math.
3 to 6 months to hire one senior AI engineer
Sourcing, interviewing, closing, and onboarding consume a quarter before a line of code ships.
Generic offshore teams with no outcome ownership
Hours billed, deliverables vague, governance optional. Your engineering lead becomes the project manager.
Prototypes that never reach production
Most AI initiatives stall in a staging environment. Models are not deployed. Return on investment never materialises.
Security and compliance sorted after the fact
Data access controls, non-disclosure agreements, and posture reviews are an afterthought. Legal gets involved late.
Engineers active in your codebase in 11 days
From signed agreement to first PR merged. No 90-day ramp. No orientation weeks that do not count toward delivery.
Outcome service level agreement, not just a billing rate
Each engagement has a defined delivery expectation. Sprint governance is included. You know what ships each fortnight.
Production-grade AI, not proof-of-concept demos
The bench includes MLOps engineers whose sole job is getting models live, monitored, and maintainable by your internal team.
Security posture review included at day one
Non-disclosure agreements, data processing agreements, and access controls are in place before a single engineer touches your environment.
What the engagement looks like for you
Select your role to see how AI staff augmentation services map to your specific goals and team structure.
Your architecture stays yours. Your velocity stops being the bottleneck.
You have an AI roadmap and a team that is at capacity. Every quarter that passes without shipping is a competitive gap widening. The fix is not a hiring cycle. It is embedded engineers who join your existing architecture, follow your standards, and accelerate your timeline.
11
Days to first PR
100%
Intellectual property owned by you
Day 1
Security review
- Architecture handoff session in week one. Engineers understand your stack before they write a line.
- Security and compliance posture reviewed before access is granted. Non-disclosure agreements and data processing agreements in place at onboarding.
- Engineers work in your stack, your tooling, your repository. No requirement to adopt new systems.
- Defined outcome service level agreement per sprint cycle, not just a billing rate. You know what ships each two weeks.
Your sprint board fills. Your management overhead stays flat.
You know exactly what is blocking delivery. You need engineers who join your existing sprint cadence, review their own pull requests to standard, and do not add a management layer to your week. That is the engagement model.
3.4x
Average velocity increase
70%
Senior engineers
48h
Scale up or down
- Seniority mix is tailored to your roadmap. Default is 70% senior engineers with at least one principal-level per engagement.
- Sprint ceremonies are already included. Engineers attend planning, retrospective, and review as part of the engagement, not an add-on.
- Weekly velocity and delivery reporting included. You see what shipped, what is in progress, and what is blocked before your Monday standup.
- Team size adjusts on 48-hour notice. Start with two engineers and scale to six once the sprint rhythm is established.
Models in production, not in the prototype stage.
Your initiative needs ML engineers who understand data pipelines, model lifecycle, and production deployment. Not generalist developers who will learn MLOps on your sprint. The bench covers every specialisation your project actually needs.
6
Average models to production
Sprint 1
MLOps setup
Full
Ownership transfer
- ML engineers, data engineers, MLOps specialists, large language model application developers, and computer vision engineers, all on the bench.
- Model monitoring and drift detection configured in sprint one. You get production-grade observability from the start.
- Full documentation and handoff package at engagement close. Your team runs the models independently from day one after handoff.
- Data privacy controls and compliance configurations are included in onboarding. Not a separate line item.
Custom AI staff augmentation services in three engagement models
Every engagement includes sprint governance, outcome service level agreements, and a principal engineer who owns architectural decisions. Choose the model that matches your current initiative.
11
Days from contract to first PR merged
No orientation months. Engineers are in your codebase and shipping by the end of week two.
70%
Senior engineers in the default seniority mix
Every engagement includes at least one principal-level engineer who owns architecture and code review standards.
SLA
Outcome service level agreement included at no extra cost
Each sprint has a defined delivery expectation. If a sprint misses, the next sprint is adjusted to compensate. No surcharges.
From brief to first PR
Day 1
Brief submitted
You describe the initiative, team size, and stack.
Day 3
Proposal delivered
Team composition, seniority mix, and engagement model scoped and priced.
Day 7
Contracts signed
Non-disclosure agreements, data processing agreements, access agreements, and Sprint Zero scheduled.
Day 11
First PR merged
Engineers are in your stack and shipping. Outcome tracking begins.
Embedded Sprint Team
2 to 6 AI engineers embedded in your existing sprint cadence. Engineers attend all ceremonies and report inside your project management tooling.
- Duration: 3 to 12 months
- Best for: Accelerating an ongoing AI roadmap
- Team scales with 48-hour notice
AI Model Delivery Pod
ML engineers plus an MLOps specialist assigned to a specific model outcome. Defined delivery milestone. Full ownership transfer at engagement close.
- Duration: 6 to 16 weeks
- Best for: Getting a specific model to production
- Monitoring and documentation included
AI Division Build
Fractional AI director plus a full engineering team. Builds your internal AI function from zero, sets standards, and trains your team to operate independently.
- Duration: Ongoing with quarterly reviews
- Best for: Building an AI capability from scratch
- Includes hiring plan for internal team
What a dedicated technical team actually delivers
Parsons Kellogg
Enterprise CommerceLarge-scale promotional products and branded merchandise company operating across 30 storefronts with over one million inventory items.
Business challenge
Needed to scale beyond $80M annually. Data was fragmented across 30+ stores with no unified reporting layer, no real-time analytics, and manual warehouse workflows consuming engineering capacity that should have been shipping product.
What was delivered
A dedicated technical team delivered a custom Power BI analytics layer connected to Dynamics 365 enterprise resource planning software, a headless ecommerce architecture for multi-store management, and RESTful application programming interfaces integrating the enterprise resource planner, customer relationship management software, Power BI, and mobile applications.
The team owned architecture decisions, sprint governance, and delivery quality from brief to handoff. Internal teams were trained and operating independently within the engagement window. Zero replatforming required after handoff.
Result
$14M
annual revenue at start of engagement
$90M
annual revenue after dedicated team delivery
Engagement covered: Power BI analytics, headless commerce architecture, enterprise resource planning integration, RESTful application programming interface development, warehouse automation, and internal team training.
What separates outcome-led teams from generic resource supply
Other AI staffing providers bill hours. The difference shows at the end of a sprint when you ask what shipped.
Questions buyers ask before signing
Straight answers on placement speed, seniority, tooling, intellectual property, and scaling, before you sign.
Engagements begin within 11 days of a signed agreement. Your first engineers are active in your codebase and attending sprint ceremonies within the first two weeks. The brief-to-proposal cycle takes 3 days.
The default mix is 70% senior engineers and 30% mid-level practitioners. Every engagement includes at least one principal-level engineer who owns architectural decisions and establishes code review standards.
Yes. Engineers join your existing stack, source control system, and project management tooling from day one. There is no requirement to adopt new systems or change your sprint cadence.
Within the first two weeks, you have a full no-questions replacement guarantee. After that, we maintain a 48-hour replacement window for the duration of the engagement.
All intellectual property produced during the engagement belongs to your organisation. Engineers sign non-disclosure agreements and data processing agreements before access is granted. A security posture review is included at onboarding.
The bench covers ML engineers, data engineers, MLOps engineers, large language model application developers, computer vision specialists, natural language processing engineers, and AI infrastructure engineers.
Yes. Team size adjusts on 48-hour notice, up or down. Engagements start with two engineers and scale to four or six once sprint rhythm is established.
Who this engagement is designed for
Good Fit
- You have a funded AI initiative and need engineers, not more planning
- Your internal team is at capacity and quarterly goals are slipping
- You need senior AI talent faster than a full recruiting cycle allows
- You want defined outcome ownership, not hours billed against a vague scope
- You plan to own and operate the systems after the engagement closes
Not The Right Fit
- You need strategy consulting to define the initiative first (that is a different engagement)
- You require offshore-only pricing with no sprint governance or outcome accountability
- You need a single freelancer with no team-level oversight or delivery structure
- Your project requires permanent on-site presence in a location we do not currently serve
Not sure? Tell us your situation and we will be straight with you. Submit your brief and we will tell you within 24 hours whether this is a fit.
Tell us about the initiative and where your team is blocked
We scope every engagement before a single engineer is placed. No generic pitch. No commitment to receive the proposal.
Call within 48 hours. A senior advisor reviews your brief and calls to clarify scope and seniority requirements.
Proposal in 3 days. Team composition, seniority mix, engagement model, and pricing are scoped and delivered as a detailed proposal.
First PR in 11 days. From signed agreement to your first engineer shipping in your codebase.
Brief received
We will review your initiative and call within 48 hours to clarify scope and seniority requirements. A scoped staffing proposal will be with you within 3 business days.
Call in 48 hours
Proposal in 3 days
45+ engagements
Intellectual property owned by you
Senior AI engineers in your codebase in 11 days
Not a pitch. Not a retainer. A scoped proposal for enterprise AI staff augmentation services based on your actual initiative. Submit your brief and a senior advisor will call within 48 hours.
