CUSTOM AI FOR DENTAL GROUPS

Purpose-built AI
for your specific dental workflows

Off-the-shelf dental software doesn't account for your DSO's specific credentialing process, your referral network intake form, or your group's proprietary treatment protocol documentation. Agaro engineers custom AI tools scoped to the exact workflow you can't configure away in a vendor product.

Request a Briefing
Custom AI Software
Active engagements Last 14d

Dashboard

Operations summary across every custom ai software module deployed in your tenant — last 24 hours.

Overview
MODULES LIVE
6
custom ai software stack
EVENTS · 24H
3.0K
99.7% first-pass
CONTAINMENT
87%
in-band
AUDIT TRAIL
100%
replayable
OPERATIONS · LAST 24H HANDLED ESCALATED
3,010 events handled
00:0004:0008:0012:0016:0020:00
MODULES · HEALTH 6 live · tap a row to inspect
Engagements 5 Live
Models 24 Live
Evals · CI 142 Live
Builds 142 Live
Pipelines 24 Live
Handover 4 Live
RECENT ACTIVITY Live feed · updated 1m ago
14:45
Engagements ON TRACK ENG-049 · Legal review pipeline — Phase 3/4 · hardening
14:38
Models PROD agaro/legal-review-7b — fine-tuned · ENG-049
13:31
Evals · CI PASS legal-review · safety — 482/482 · 0 regressions
13:24
Builds PASS agaro/operator-console#1840 — staging deploy
12:17
Pipelines LIVE legal-rag · ingest — 4,128 docs/d
12:10
Handover CLOSING ENG-047 · Claims triage — 90% transferred · pair wk 12
DELIVERY · ENGAGEMENTS

Engagements

Discovery → prototype → hardening → transfer. Every engagement runs the same four-phase rhythm.

Overview
engagements.ts eval.suite model.json

                    // Engagements
                    // Discovery → prototype → hardening → transfer. Every engagement runs the same four-phase rhythm.
                    export const engagements = {
                    
                          live_engagements: '5',
                      
                          avg_cycle: '14 wks',
                      
                          on_schedule: '5/5',
                      
                          handover_90d_tail: '4 active',
                      
                    };
                  
ENGAGEMENTS · LIVE CI · main
ENG-049 · Legal review pipeline Phase 3/4 · hardening wk 9 of 12
ENG-048 · Underwriting agent Phase 2/4 · prototype wk 6 of 14
ENG-047 · Claims triage Phase 4/4 · transfer 90d tail
· ENG-046 · Field dispatch Phase 1/4 · discovery wk 2 of 16
AI · MODELS

Models

Fine-tuned, distilled, and adapter-trained models in registry — including parameter-efficient methods that fit your inference budget.

Overview
models.ts eval.suite model.json

                    // Models
                    // Fine-tuned, distilled, and adapter-trained models in registry — including parameter-efficient methods that fit your inference budget.
                    export const models = {
                    
                          models_in_registry: '24',
                      
                          adapter_sets: '142',
                      
                          avg_inference: '142ms',
                      
                          ip_ownership: '100%',
                      
                    };
                  
MODEL REGISTRY CI · main
agaro/legal-review-7b fine-tuned · ENG-049 v1.4
· agaro/uw-classifier-3b distilled · ENG-048 v0.9
agaro/claims-triage-7b LoRA · ENG-047 v2.1
agaro/extraction-3b QLoRA · ENG-045 v1.0
QUALITY · EVALS · CI

Evals · CI

Custom evals you run continuously, so model regressions are caught in CI, not in the field.

Overview
evals-ci.ts eval.suite model.json

                    // Evals · CI
                    // Custom evals you run continuously, so model regressions are caught in CI, not in the field.
                    export const evals_ci = {
                    
                          eval_suites: '142',
                      
                          coverage: '84%',
                      
                          pass_rate: '98.4%',
                      
                          regressions_caught: '4',
                      
                    };
                  
EVAL · CI RUNS CI · main
legal-review · safety 482/482 · 0 regressions 4m 12s
uw-classifier · accuracy 142/142 · 0 regressions 1m 48s
claims-triage · jailbreak 88/88 · 0 successful 6m 18s
extraction · pii 4,128 docs · 0 leaks 14m
DELIVERY · BUILDS

Builds

CI pipelines, build artefacts, and deploys for every engagement — containers, IaC, and runbooks shipped together.

Overview
builds.ts eval.suite model.json

                    // Builds
                    // CI pipelines, build artefacts, and deploys for every engagement — containers, IaC, and runbooks shipped together.
                    export const builds = {
                    
                          builds_24h: '142',
                      
                          build_time_avg: '4m 11s',
                      
                          deploys: '12',
                      
                          crash_free: '99.94%',
                      
                    };
                  
BUILDS · CI CI · main
agaro/operator-console#1840 staging deploy 4m 12s
agaro/legal-rag#982 prod deploy 3m 28s
· agaro/uw-svc#412 integration tests running
agaro/claims-api#284 lint + type 1m 12s
AI · PIPELINES

Data pipelines

Retrieval, embeddings, ingestion, and training pipelines — wired into your sources with backpressure and replay.

Overview
pipelines.ts eval.suite model.json

                    // Data pipelines
                    // Retrieval, embeddings, ingestion, and training pipelines — wired into your sources with backpressure and replay.
                    export const pipelines = {
                    
                          active_pipelines: '24',
                      
                          events_24h: '14M',
                      
                          embedding_rate: '4.2K/s',
                      
                          vector_store: '88GB',
                      
                    };
                  
PIPELINES · LIVE CI · main
legal-rag · ingest 4,128 docs/d pgvector
uw-features · CDC Postgres → feature store continuous
claims-eval · train weekly · LoRA Sun 02:00
extraction · drift detect continuous 14d window
DELIVERY · HANDOVER

Handover

Architecture docs, ADRs, runbooks, and 90-day support tail — knowledge transfer baked in from week one.

Overview
handover.ts eval.suite model.json

                    // Handover
                    // Architecture docs, ADRs, runbooks, and 90-day support tail — knowledge transfer baked in from week one.
                    export const handover = {
                    
                          engagements_transferring: '4',
                      
                          pair_sessions_30d: '24',
                      
                          runbooks: '14',
                      
                          90_day_tail: '3 active',
                      
                    };
                  
HANDOVER · ACTIVE CI · main
ENG-047 · Claims triage 90% transferred · pair wk 12 tail wk 1
ENG-049 · Legal review Architecture review wk 8 on plan
ENG-045 · Extraction Runbook drafted wk 11
ENG-040 · Drone routing 90-day tail · 60d remaining support
Brief · operator console v1 Discovery
Draft Deploy agent
Scope · web + iOS + AI assist
L. Tanaka Architecting…
Capabilities

What custom ai software actually does for dental.

01

Domain-tuned models

Fine-tuning, distillation, and adapter training on your data — including parameter-efficient methods that fit your inference budget.

02

Agent systems

Multi-step, tool-using agents with planning, verification, and the supervision loops your operators trust.

03

Evaluation harnesses

Custom evals you run continuously, so model regressions are caught in CI, not in the field.

04

Safety & red-team

Adversarial testing against jailbreaks, prompt injection, and the failure modes specific to your domain.

05

Deployment-ready code

We ship containers, infrastructure-as-code, runbooks, and the documentation your SRE team needs to operate it.

06

Knowledge transfer

Pair with your engineers from week one. By the end, your team owns and operates what we built together.

Specifications

Engineered to a standard, not a slogan.

Engagement Length
8–24 weeks typical
Discovery, prototype, hardening, transfer. We don't ghost after the demo.
Licensing
Standard licensed delivery
Specific commercial terms — including any data, configuration, or co-developed asset rights — scoped per engagement.
Team
Senior applied AI engineers
3–8 person pods led by a tech lead with shipped production AI experience.
Stack
Cloud-first
AWS, Azure, or GCP — wired to your existing identity, networking, and data systems.
Frameworks
Pragmatic
PyTorch, JAX, vLLM, TensorRT — picked for fit, not fashion.
Handover
Runbook-complete
Architecture docs, ADRs, runbooks, and a 90-day support tail.
Frequently Asked Questions · Custom AI Software for Dental

What dental buyers ask about custom ai software
— before they sign.

Engagements start with a scoped discovery session — usually two to four hours with your operations and clinical leadership — where we document the exact workflow, data sources, and success criteria. We then deliver a fixed-scope proposal. Most dental-specific builds take six to fourteen weeks depending on integration complexity. You own the output.
A scoped pilot runs about 30 days. Full production cutover is typically 8–12 weeks, including integration with the systems dental practices and DSOs already run, plus the handoff and escalation flows your team expects.
Yes. Agaro is SOC 2 Ready and HIPAA Ready, with role-based access controls, encryption at rest and in transit, per-deployment data isolation, and full audit logging — so the deployment meets the security bar dental practices and DSOs are held to.

Deploy custom ai software
for your dental team.

A 30-day pilot will show you the integration shape, the operator experience, and the audit trail your team will ask about — delivered by a senior engineering team that ships AI to production for dental practices and DSOs.

Begin Pilot