Hire a Dedicated AI Development Team

AI dedicated teams fit products where model behavior is the feature. Staff augmentation fits when you have an ML lead and need senior implementers.

Siblings Software assembles dedicated ai pods with a tech lead, senior engineers, QA inside the sprint, and a delivery lead who joins your ceremonies. Typical ramp is two to three weeks to sprint one. Monthly retainers run USD 12K to 60K depending on team size. If you only need one or two engineers embedded in your squad, see our AI staff augmentation page.

Reviewed by Javier Uanini, Founder and CEO · updated 2026-06-16.

Production Model Test framework diagram for AI dedicated team decisions

Talk to a delivery lead

When a dedicated AI team makes sense

AI dedicated teams fit products where model behavior is the feature. Staff augmentation fits when you have an ML lead and need senior implementers.

Contrast with staff augmentation when you already run sprint rituals and need extra senior hands. Contrast with project-based outsourcing when scope is fixed and short. The dedicated development team model fits multi-quarter roadmaps with one accountable pod.

The Production Model Test

We publish the Production Model Test so buyers can self-diagnose before the discovery call. Three honest answers mean proceed with a dedicated pod. Any no means staff augmentation or a narrower fixed scope first.

Q1. Is there a golden eval set?

Without labeled evals, model changes are guesswork.

Q2. Are costs per request bounded?

Token budgets and caching belong in sprint zero, not after the invoice shock.

Q3. Is human review in the loop for high-risk outputs?

Regulated domains need escalation paths, not raw LLM answers.

Team composition

AI tech lead, two senior ML engineers, one backend engineer for serving, one data engineer, QA for eval automation, fractional security reviewer.

Lean pod (4 people)

Tech lead plus two seniors and shared QA. USD 12K to 22K per month.

Standard pod (6 to 8 people)

Full leadership stack plus DevOps or SRE as needed. USD 24K to 42K per month.

Program (10 plus people)

Multiple pods with shared platform bench. USD 45K to 60K plus per month.

See also our ai development outsourcing service page and sibling dedicated team offering.

Your first 30 days

Days 1 to 5

Discovery, access, architecture read, and a written team proposal with sprint-zero goals.

Days 6 to 12

Interviews, security onboarding, CI and observability wired, Definition of Done agreed.

Days 13 to 21

Sprint zero completes: first merges to staging, test harness green, runbook draft for top alerts.

Days 22 to 30

Sprint one delivery against an agreed goal. Weekly demo and retro with your stakeholders.

External references we cite in sprint zero include platform.openai.com, www.nist.gov, modelcontextprotocol.io.

Governance

Your product owner owns priorities. Our delivery lead runs sprint planning, standups, and stakeholder updates. The tech lead owns architecture decisions, code review SLA, and production readiness sign-off. We document decisions in short ADRs in your repository.

Escalation path: engineer to tech lead to delivery lead to your engineering director within one business day. IP is work-for-hire under US-style agreements. Source code lives in your org. We join your Slack, Jira or Linear, and on-call rotation when scoped.

Quality standards

Definition of Done includes peer review, automated tests, updated contracts or docs, observability hooks, and staging deploy. QA automation runs inside the sprint, not after it. We track escaped defects and sprint goal attainment on a shared dashboard.

For ai engagements we align with OWASP baselines where applicable, enforce dependency scanning in CI, and refuse to close stories that fail agreed quality gates.

Pricing

Dedicated ai teams bill monthly and predictably within the USD 12K to 60K band:

Small pod

USD 12K to 22K / month

Four to five people. Initial three-month commitment, then month to month.

Standard pod

USD 24K to 42K / month

Six to eight people with full leadership and QA.

Program

USD 45K to 60K+ / month

Multi-pod engagements with platform support.

Two-week satisfaction guarantee on every seat. Scale down with 30-day notice after the initial period.

Example engagement

Lexicon Legal AI. Legal research assistant with RAG over 1.8M documents. Six-person AI pod added eval harness, prompt versioning, and OTel tracing. Hallucination rate on golden set 19% to 4%, p95 query 8.2s to 2.1s. USD 45K/month.

What we would do differently: Freeze embedding model choice until eval set exists. We re-embedded twice unnecessarily.

For a published reference see our case study.

Frequently Asked Questions

Run the Production Model Test on the discovery call. If two or three answers point to sustained ownership, a pod with delivery lead and QA beats adding individuals. AI dedicated teams fit products where model behavior is the feature. Staff augmentation fits when you have an ML lead and need senior implementers....

The Production Model Test is the named checklist our AI pods use before committing scope. Q1. Is there a golden eval set? Without labeled evals, model changes are guesswork.. Q2. Are costs per request bounded? Token budgets and caching belong in sprint zero, not after the invoice shock..

Discovery takes three to five days, interviews five to seven days, sprint zero in week two or three. Most pods merge first production changes by week four.

Small pods run USD 12K to 22K per month. Full product pods run USD 24K to 42K. Multi-pod programs run USD 45K to 60K or more. All brackets include delivery leadership and a two-week satisfaction guarantee.

Your product owner sets priorities. Our tech lead owns code review SLAs, Definition of Done, and release readiness. Delivery lead runs ceremonies and weekly status.

Adding seats takes one to two weeks. Scaling down requires 30-day notice after the initial commitment period.

OUR STANDARDS

Measured outcomes over slide decks. Reversible changes over heroic releases.

Every ai engagement ships with visible quality gates, weekly demos, and honest reporting on the metrics we agreed to in discovery.