Plug autonomous, battle-tested senior AI engineering squads directly into your standups, repos, and roadmap in 14 days. Zero hiring lag, zero headhunter fees, and 100% IP ownership.
Each pod features autonomous tech leadership, senior production specialists, and full tooling synchronization with your repository.
The squad embeds directly into your Slack/Discord, joins daily standups, reviews and is reviewed in your GitHub/GitLab org, and pushes to your cloud infrastructure under your IAM rules.
Hiring AI engineers one-by-one is notoriously slow and drains engineering management bandwidth. Squads give you instant senior production power without the recruiting quagmire.
Senior only means the door is notoriously difficult to open. We do not hire generalists who learned prompt engineering yesterday.
Candidates are handed broken production repositories: hallucinating RAG retrieval loops, unoptimized vector indexes, and leaking memory pools. We evaluate how they debug, benchmark, and document under pressure.
A 90-minute live session with our Principal Architects dissecting latency bottlenecks, semantic router topologies, quantization tradeoffs (FP16 vs INT4), and GPU serving concurrency.
We assess clear RFC authoring, structured GitHub pull request breakdowns, and concise verbal communication during simulated daily standups and sprint planning rituals.
Before joining the active bench for client deployment, engineers ship real internal features in simulated client environments to verify commit speed, autonomy, and security hygiene.
Whether you need a self-directed dedicated squad, individual senior staff augmentation, or milestone-scoped builds.
| Dimension |
Dedicated AI Squad
MOST POPULAR
|
Staff Augmentation | Fixed-Scope Milestone |
|---|---|---|---|
| Team Composition | Full autonomous pod: Principal Lead + 2-4 Senior Engineers + Eval QA | 1 to 3 individual Senior AI specialists plugged into your teams | Project manager led deliverable squad focused on strict spec |
| Management Overhead | Minimal: Tech Lead drives delivery, reviews PRs, and reports at standup | Moderate: Managed by your existing engineering managers | Zero: Deliverables managed strictly against milestones |
| Roadmap Flexibility | Continuous: Pivot backlog weekly as model discoveries occur | Continuous: Assign tickets according to your internal sprint cycle | Rigid: Scope changes require change orders and timeline recalculation |
| Commitment & Terms | Month-to-month retainer. Scale seats with 30-day notice | Month-to-month per seat. Zero placement fees | Milestone-based progress billing tied to acceptance criteria |
| Ideal For | Companies building core AI product offerings needing speed without hiring delay | Teams missing a specific capability (e.g. fine-tuning or vector search) | Well-defined proofs-of-concept or discrete migration projects |
Design high-throughput LLM architectures with semantic caching, streaming protocols, token optimizations, and guardrail layers using Llama 3, Claude 3.5, and OpenAI.
Build resilient multi-agent swarms with stateful graph memory, human-in-the-loop triggers, tool calling, and self-correcting execution pipelines.
Construct multi-stage retrieval pipelines: hybrid sparse/dense search, reciprocal rank fusion (RRF), contextual chunking, and metadata filtering.
Domain adaptation via LoRA/QLoRA, DPO/RLHF alignment, synthetic data generation, and model quantization for cost-efficient self-hosted inference.
Zero-shot object detection, OCR extraction on complex financial documents, video frame analytics, and multimodal embeddings for real-time edge processing.
Private cloud model deployments (AWS VPC, GCP, Azure), GPU autoscaling, continuous synthetic eval telemetry, and SOC2 compliant data boundaries.
Everything you need to know about integrating our dedicated engineering squads.