AI that actually ships,
in businesses that actually run.

Production AI inside the systems your business actually runs on. Citation-grounded. Cost-disciplined. Built for regulated workloads. Architected to outlast the AI hype cycle, not to ride it.

Book discovery callBook a call→
Live · production
XYTGHHFRSSARX5-D quality · 3 checkpointsPublish
Blabberfish Agentic content engine · we run it on ourselves
60+ Sources monitored 24×7
11yrs
Shipping production software
300+
Systems in production
30
Engineers & architects
15+
Countries served
4+
Regulatory regimes — HIPAA, DPDPA, RBI, IRDAI

The AI market is full of noise. We are the signal.

Most "AI experts" you've met this year can't tell you what a vector database costs to run at scale, how a RAG pipeline fails silently, or what happens to your data after it leaves your network. They sell you a vision. We sell you a system.

We've spent the last several years building production AI inside the systems businesses actually depend on — HIPAA-compliant medical platforms, legal drafting engines with citation guarantees, ERPs with embedded OCR pipelines, and edge AI running on consumer wearables. We know what works, what costs more than it should, and where AI quietly breaks the moment it leaves a demo.

When you work with us, you get an engineering team — not a consulting deck. We'll tell you which parts of your business are ready for AI, which parts are not, what it will cost to run for the next five years, and where the risks actually sit.

LiveBuildTest & hardenDeploy
We build, we don't just advise

Every recommendation is something we have shipped.

Our case studies are real systems running today, not pilots that died after the press release. The architecture we recommend is the architecture we've debugged at 2am.

Modelled budgetY1Y2Y3TokensInfra
We're honest about cost

3-year TCO modelled before you sign.

API tokens, infrastructure, retraining, the lot. AI is not free, and recurring costs catch most businesses off guard. We'd rather lose a deal on day one than break a budget in year two.

Y1Y2Y3Y4Y5Y6Y7Y8Y95-year baseExtend to 8–9
We build for the long run

Designed for a 5-year base lifecycle.

Extendable to 8–9 years with minor yearly maintenance. No rewrites every 18 months. Your investment lasts.

We don't sell decks.
We sell systems.

A demo proves curiosity. A working system in production proves competence. Everything below is the second kind.

Book discovery callBook a call→

Five questions every CXO asks about AI. We answer them honestly.

With the architecture, with the numbers, and with the case study where we shipped it. Click any question to see how we think about it.

No. Every AI system we design treats data residency, encryption, and tenant isolation as architectural requirements, not afterthoughts. We have built HIPAA-compliant systems where PHI never leaves the AWS BAA boundary, and on-premise legal platforms for government deployments. We design for your regulatory regime — not a generic one.

0

PHI bytes that traverse the public internet in our default healthcare architecture

From · IMEAI · Phlebnet bot
4-layer

PHI redaction before any LLM call: regex + field scrub + Comprehend Medical + Bedrock Guardrails

From · Phlebnet AI support bot

Projects we've shipped.
Check out the work.

Four flagship systems — picked because they cover four different CXO mindsets and four different proof points. The full archive is twelve deep.

Constraint-aware dispatch

Phlebtrax.ai

Mobile clinical dispatch · home phlebotomy

Solves the kind of NP-hard routing problem most "AI dispatch" vendors can't articulate, let alone implement. Capacity-constrained K-Means + 11-constraint filtering + OR-Tools VRPTW + 2-opt metaheuristics. Every assignment auditable.

25–40%
Fleet-mile reduction
<30sec
STAT re-routing
95%
Map-API cost cut
OR-Tools VRPTWK-MeansOSRMGoogle MapsXGBoostPostgreSQL
Talk to the team that built thisTalk to us →
Three-stage LLM orchestration

Wazzy

Conversational seizure diary · Apple Watch + iPhone + Web

Planner + Agent + Parser on Claude Haiku via Bedrock. Versioned prompts as first-class data. Graceful degradation when the LLM is unavailable. Logs cost on every turn — so admins see real-time spend per patient.

$0.15/mo
Typical patient LLM cost
3-stage
Orchestration per turn
watchOS · iOS · Web
From one backend
Claude HaikuAWS BedrockFastAPIDocumentDBwatchOSCore ML
Talk to the team that built thisTalk to us →
Source-grounded ideation + persona-authentic authoring

Blabberfish

Agentic content engine · we run it on ourselves

Monitors 60+ technology sources continuously via OpenClaw. Generates source-grounded ideas, drafts in the voice of five real engineers, scores on five quality dimensions. Human-in-the-loop at three checkpoints. Yes, the name is on purpose.

60+
Sources monitored 24×7
5-D
Quality scoring per draft
3
Human checkpoints
OpenClawMulti-model routingPersona modelsFact-check graph
Talk to the team that built thisTalk to us →
Closed-loop clinical RAG inside the BAA boundary

IMEAI

HIPAA-compliant medical AI · Independent Medical Examinations

Every AI component — OCR, medical NLP, embeddings, LLM — runs inside one signed AWS BAA boundary. PHI never leaves. Hybrid RAG with Reciprocal Rank Fusion. Source-cited evidence on every claim with clickable bounding boxes.

2–4hr → 5–10min
Fact extraction per case
100%
PHI inside the AWS BAA boundary
100%
Claims cited & verifiable
AWS BedrockTextractComprehend MedicalpgvectorTitan + Claude
Talk to the team that built thisTalk to us →

We don't claim expertise we haven't earned.

Seven verticals where we've shipped real AI systems with real users in production. Click any row to see what we ship inside it.

  • PHI inside a single signed AWS BAA boundary
  • Citation-validated clinical RAG with bounding-box evidence
  • Constraint-aware dispatch for home-based clinical services
  • On-device seizure / AFib / fall detection on Apple Watch & Wear OS
  • AI fax triage that splits multi-patient transmissions automatically
IMEAIWazzyPhlebtrax.aiPhlebioAIPhlebnet AI support botWearable Edge AI
● In production

Healthcare & Life Sciences

HIPAA-compliant medical AI · clinical document intelligence · mobile dispatch · edge AI on wearables.

  • PHI inside a single signed AWS BAA boundary
  • Citation-validated clinical RAG with bounding-box evidence
  • Constraint-aware dispatch for home-based clinical services
  • On-device seizure / AFib / fall detection on Apple Watch & Wear OS
  • AI fax triage that splits multi-patient transmissions automatically
Shipped as
IMEAIWazzyPhlebtrax.aiPhlebioAIPhlebnet AI support botWearable Edge AI

A predictable process, designed around your risk — not ours.

Most AI projects fail in the first six weeks because the discovery was rushed and the scope was wrong. Our 5-phase model exists to prevent that. Discovery and architecture before a single line of code is written. Working features every week after that.

2–4 weeks

Discovery & Architecture

We map your actual workflow, identify the highest-leverage AI moments, design the pipeline end-to-end, and model 3-year TCO before development begins.

2–3 weeks

Design & Prototype

High-fidelity UI/UX, AI pipeline architecture, database schema, and a clickable prototype. If the design is wrong, this is where we fix it.

ongoing

Build in Weekly Increments

Working features every week. You review, react, redirect. No black box. Most projects ship a usable v1 within 6–8 weeks.

1–3 weeks

Test & Hardening

Functional, load, OWASP, AI-accuracy testing with your real data. UAT with your team before production.

ongoing

Deploy, Train, Support

Production deployment, team training, documentation, and tiered support — P1 response in 30 min, 24×365. Senior engineers, not BDRs.

2–4 weeks

Discovery & Architecture

We map your actual workflow, identify the highest-leverage AI moments, design the pipeline end-to-end, and model 3-year TCO before development begins.

2–3 weeks

Design & Prototype

High-fidelity UI/UX, AI pipeline architecture, database schema, and a clickable prototype. If the design is wrong, this is where we fix it.

ongoing

Build in Weekly Increments

Working features every week. You review, react, redirect. No black box. Most projects ship a usable v1 within 6–8 weeks.

1–3 weeks

Test & Hardening

Functional, load, OWASP, AI-accuracy testing with your real data. UAT with your team before production.

ongoing

Deploy, Train, Support

Production deployment, team training, documentation, and tiered support — P1 response in 30 min, 24×365. Senior engineers, not BDRs.

What we will and will not do.

We get asked to do things every week that we will not do. Here is our charter, in plain language. If any of this conflicts with what you're looking for, we are not your team — and we'll say so on the discovery call.

We will

do this
  • ✓Tell you when AI is the wrong answer for your problem
  • ✓Show you the 3-year TCO before you sign anything
  • ✓Insist on human-in-the-loop for high-stakes decisions
  • ✓Design for graceful failure — your business doesn't stop if our AI does
  • ✓Cite our sources in every AI-generated output, so users can verify
  • ✓Hand over full source code and documentation at project close
  • ✓Tell you honestly when a feature would cost more than it's worth

We will not

no exceptions
  • ✕Build AI that decides without human review in high-stakes contexts
  • ✕Use your data to train models that benefit other clients
  • ✕Lock you into infrastructure you cannot migrate off
  • ✕Recommend the expensive model when a cheaper one does the job
  • ✕Promise capabilities we cannot demonstrate in working code
  • ✕Hide recurring costs inside vague 'infrastructure' line items
  • ✕Sell you a pilot we have no plan to scale

30 minutes. No deck.
Just a conversation.

If you're thinking about AI for your business and you want to talk to someone who will tell you the truth — including when AI is the wrong answer — book a call. We'll spend 30 minutes understanding what you're trying to do, what you're worried about, and whether we're the right fit.

No sales pitch. No deck. No follow-up pressure.
Calls are taken by a senior engineer who has shipped the work, not a BDR.
You'll walk away with at least one practical idea — even if we never work together.
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