HyperNest Labs provides on-demand engineering team members and consulting services to help early-stage startups build world-class products. Where Elite Engineering Teams Take Flight.
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Ship AI features with measurable ROI
We help founding teams go from idea → shipping AI copilots, agents, and intelligent automation. From model selection and evaluation to latency-optimized infra, we own the entire build alongside your product team.
<700ms
Latency achieved on agent workloads
18
Fine-tuned models deployed
42%
Cost savings via prompt optimization
$2M+
AI-related ARR unlocked
Every startup wants to showcase AI, but most teams struggle to move beyond demos. We embed with your PMs and engineers to prioritize the highest-leverage AI workflows, rapidly test with users, and harden them for production.
Our background spans applied research and high-scale product engineering so we can advise on data strategy, prompt engineering, evaluation harnesses, and infra choices in one pod.
Use-case discovery workshop with scoring across feasibility and impact
Reference architecture for orchestration, retrieval, and monitoring
Prompt libraries plus automated evaluation suite
Data ingestion + labeling pipeline with governance controls
Latency + cost dashboards wired into product analytics
Security review covering data retention, SOC2, and GDPR considerations
Runbooks for hallucination handling and fallback logic
Knowledge-base integration or vector store management
Hands-on experience launching AI recruiters, assistants, and scoring engines
Bench of engineers fluent in LangChain, LlamaIndex, Pinecone, Weaviate, and bespoke infra
LLMOps best practices including eval harnesses, prompt versioning, and cost monitoring
Partnership mindset — we ship features not research reports
Ability to integrate with regulated industries (healthcare, fintech, HR)
Tight collaboration with your GTM team to craft demos and sales enablement
Day 0-7
Map workflows, understand data access, and size the opportunity before touching a model.
Day 8-30
Rapid sprints to design prompts, build retrieval pipelines, and run evals with real data.
Day 31+
Productionize infra, implement monitoring, and train your team to operate the stack.
Sprint 1: Discover
Clarify jobs-to-be-done and data constraints.
Sprint 2: Build
Ship a working slice inside your product.
Sprint 3: Scale
Expand coverage and reduce cost per interaction.
Proof it works
We architected and shipped an AI recruiter that conducted 24/7 interviews, summarized outcomes for hiring managers, and unlocked a $2M ARR Job Boost product line.
$2M+
New ARR
72% → 99.97%
LinkedIn posting success
<0.5s
Response time
99.99%
Availability
“HyperNest built the AI backbone of our product and kept a relentless focus on user value and reliability.”
Aravind Srinivas
Former Head of Engineering, PyjamaHR
Which models and providers do you support?
We are provider-agnostic and have production experience with OpenAI, Anthropic, Google Vertex, Azure OpenAI, open-source models on AWS/GCP, and custom LoRA fine-tunes.
Do you handle data labeling and evaluation?
Yes. We set up human-in-the-loop review tools, build rubrics, and create automated regression suites so you can ship confidently.
Can you embed with our existing ML team?
Absolutely. We often complement internal ML researchers with product-focused engineers who can bridge infra, UX, and GTM needs.
How do you price AI engagements?
We scope around 2–4 week sprints. Each sprint bundles discovery, build, and enablement work at $35/hour with clear deliverables before kickoff.
We’ll audit your architecture, map out an engagement, and plug in team members within days.
Plan an AI sprint