Full‑stack builds • On‑prem LLMs • Agentic automation
Ship production AI + software in weeks — not months.
Hive partners with founders and engineering leaders to design, build, and deploy secure systems: modern full‑stack products, private/on‑prem LLM platforms, and agentic workflows that accelerate delivery.
- Speed
- Rapid discovery + prototype sprint to validate fast.
- Production
- CI/CD, observability, security, and scalable architecture.
- Privacy
- On‑prem options and data‑safe AI designs by default.
Typical engagements: AI readiness & architecture, 2‑week prototype sprint, and end‑to‑end product builds.
How we work
Fast, secure delivery—without the chaos
You’re not hiring a generic dev shop. You’re onboarding a technical partner built for high‑stakes execution.
Outcome‑driven engineering
We start with the business goal, map constraints, then design the smallest production‑safe path to value.
AI that survives production
RAG, evaluation, monitoring, cost controls, and human‑in‑the‑loop—built in from day one.
On‑prem when you need it
Private model serving, security boundaries, and deployment patterns for regulated or sensitive data.
Agentic workflows for speed
We use agentic coding systems to accelerate delivery while keeping quality gates, review, and tests strict.
What we build
Three ways we help you ship
Pick the engagement that matches your risk profile: validate fast, deploy privately, or scale the product end‑to‑end.
AI Systems & LLM Deployment
RAG, private inference, fine‑tuning, and evaluation—built for real users.
- On‑prem / VPC model hosting & serving
- RAG architecture + data pipelines
- Prompting, tools, and function orchestration
- Evals, observability, and guardrails
- Cost & latency optimization
Full‑Stack Product Engineering
From database to UI to CI/CD—systems engineered to scale.
- MVP → production buildout
- Backend services & APIs
- Frontend web apps (Next.js)
- DevOps, infra, and deployment automation
- Security hardening & performance tuning
Agentic Automation & Copilots
Autonomous workflows that ship code, move data, and reduce busywork.
- Internal copilots for teams & ops
- Multi‑agent workflows with tool use
- Ticket → PR automation pipelines
- Human‑in‑the‑loop approvals
- Auditability & compliance controls
Need a low‑risk starting point?
Start with a 2‑week Prototype Sprint or an On‑Prem LLM Feasibility Assessment.
Proof
Products and assets we’ve shipped
A few examples of the systems we build: privacy-aware AI, developer tooling, and production platforms.
Cuelis
Command your prompts. Ship faster together.
The cockpit for reusable AI prompts: organize, version, compare, and share—without the chaos.
- Library: searchable, tagged, versioned
- Workflows: chain prompts + evals without spaghetti
- Sharing: one link to keep teams in sync
- No login needed for demo
Vektaris
Scalable & Secure Vector DB Platform
Effortlessly store, manage, and query embeddings for AI-driven applications. Built for performance and scale.
- Isolated, high-performance canisters
- Effortless embedding storage & queries
- Advanced key-based security
- Decentralized storage on ICP
FraudBuster AI
Secure Your Transactions
Protect your online payments with real-time fraud detection by Hive Forensics. Tuned for low latency and high signal.
- Realtime risk scoring
- Adaptive models for new patterns
- PCI-aware deployment
- On-prem or hybrid
KnoLo Core
Local-first Knowledge Base for Small LLMs
Package documents into a compact .knolo file and query them deterministically — no embeddings, no vector DB, no cloud.
- Deterministic queries
- Offline / on-device friendly
- .knolo packaging format
- Tiny footprint
Want a case study in your domain?
Share your constraints (security, infra, timeline) and we’ll propose an execution plan.
Get a planPredictable delivery
A process designed to reduce risk
Most AI projects fail in production because they skip evaluation, data contracts, and operations. We don’t.
1) Technical discovery
30–60 minutes to map the problem, constraints, stakeholders, and success metrics.
- Scope + assumptions
- Risk list
- Recommended engagement (sprint vs build)
2) Architecture blueprint
We design the system like we’re going to operate it: security boundaries, data flows, observability, and cost.
- Solution architecture
- Milestone plan
- Build estimate + team plan
3) Prototype sprint
Validate the hardest part first: model choice, data access, latency, UX, and evaluation.
- Working prototype
- Evals + baseline metrics
- Go/no‑go decision
4) Production hardening
Turn the prototype into a system your team can trust: tests, CI/CD, monitoring, security reviews.
- Production release
- Runbooks
- SLOs + monitoring
5) Scale and optimize
Iterate with real usage: improve accuracy, reduce cost, increase reliability, add features.
- Performance tuning
- Model upgrades
- Roadmap execution
Engagement options
Prototype Sprint • Fixed milestone build • Retainer / augmentation
Typical timelines
Prototype: 2 weeks • MVP: 6–10 weeks • On‑prem LLM: 2–6 weeks
How we measure success
Evals, latency/cost, reliability, and user outcomes—not vibes.
Resources
Make onboarding easy
If you’re evaluating vendors or trying to unblock a project internally, these resources help you move faster.
On‑Prem LLM Deployment Checklist
A practical list of decisions: model selection, GPUs, data boundaries, monitoring, and incident response.
Request the checklist2‑Week Prototype Sprint Brief
A one‑pager you can share internally to align stakeholders on scope, success metrics, and risks.
Get the brief templateAgentic Automation Playbook
Patterns for tool-using agents, approval workflows, audit logs, and reliability guardrails.
Ask for the playbookPrefer to skip the reading?
Book a call and we’ll walk through feasibility, architecture, and a delivery plan.
Book a strategy callOnboard Hive in one message
Share your constraints and we’ll respond with a plan: scope, timeline, architecture approach, and next steps.
- Response time
- Under 1 business day
- Privacy
- NDA-first, data-safe by design
- Deliverables
- Blueprint + milestones + prototype option
- Engagement
- Sprint, build, or retainer
What happens next
- We reply with clarifying questions (if needed).
- You get a recommended plan and timeline.
- We can start with a 2-week sprint to de-risk.