Miranium AI / Services

Applied AI, cloud, and
secure delivery.

Six service lines for organizations whose work gets audited. We automate document-heavy, evidence-bound processes while keeping the record, and operate the infrastructure underneath. Every engagement is delivered by the principals.

Start a conversation See the service lines
Engagement
Direct · Subcontract · Teaming
Delivery
Founder-led · delivered by our own engineers
Place of performance
Remote · on-site as required
What you get

What each line actually delivers.

The same six the home page introduces, in statement-of-work terms. Each is one posture applied to a different problem:

  • Get the evidence
  • Compute what can be computed
  • Let a model narrate rather than decide
  • Leave a record a reviewer can follow

Most engagements draw on two or three at once.

01 Document intelligence 02 AI governance 03 Applied AI systems 04 Cloud & DevOps 05 Managed web 06 Data & analytics
01

Document Intelligence & Process Automation

Structured data out of unstructured and semi-structured source documents: invoices, forms, claims, scanned records, validated against your business rules and written to your system of record. Deterministic OCR first, then a governed model pass over the result: the model reviews the result, and deterministic code writes the number into your ledger.

Scoped for a state port authority: AP invoice automation across four capture channels, three legal entities, and PeopleSoft FSCM 9.2.

  • Two-stage pipeline: OCR engine, then governed model validation
  • Every field carries a confidence score and a pointer to the region it was read from
  • Sub-threshold fields route to a review queue rather than posting
  • Exception workflow with SLA timers, severity, and escalation
  • ERP write-back, web service, component interface, or staged file
  • Immutable audit log of every extraction, coding, and approval
02

AI Governance & Assurance

The controls that let an organization put an AI system in front of a regulator. Human-in-the-loop architecture where every model-proposed action is logged as a reviewable proposal, autonomy is configurable per class of action, and the record of what was proposed, who approved it, and what ran survives the review afterward.

The approval contract runs in production in Gable Pro, our own platform, across every consequential action it takes.

  • Approval contract: propose → approve → execute, one durable ledger
  • Autonomy granted per reversibility class
  • Control mapping and assessment against the NIST AI RMF
  • Model evaluation harnesses, with published failure modes and thresholds
  • Production monitoring for drift, hallucination, and silent degradation
  • Independent review of an existing AI system, including a vendor’s
03

Applied AI Systems & Agent Engineering

Custom AI systems built the way ours are: a tool-calling agent over your own data and your own systems, with deterministic guardrails wherever the output becomes a figure, a decision, or a record. Grounded retrieval so answers cite a source passage, and abstention so the system declines rather than guessing well.

Gable Pro is the worked example, our own live, in-market platform at gable.pro, demonstrable on request.

  • Hybrid lexical and semantic retrieval over your corpus
  • Multimodal intake, photo, PDF, scan, or plain text
  • Deterministic engines behind anything that becomes money
  • Tenant isolation and capability-scoped access, per person
  • Per-turn telemetry: steps, guardrails, latency, and token cost
  • Evaluation suites that run offline, so behavior is regression-tested
04

Secure Cloud, DevOps & Platform Engineering

The infrastructure the rest of it runs on, built by engineers who have carried the pager for it. Infrastructure as code, identically provisioned environments, peer-reviewed changes, and every production change routed through the pipeline, plus the modernization work that gets a legacy platform onto that footing.

Our principals led GovCloud site reliability at Salesforce and platform infrastructure at Workday and Capital One.

  • Kubernetes and Terraform across multi-tenant environments
  • CI/CD with GitHub Actions, ArgoCD, Spinnaker; controlled promotion
  • Observability and incident response, Grafana, Splunk, ELK, PagerDuty
  • Identity integration: SSO and MFA via SAML or OIDC federation
  • Encryption in transit and at rest, least-privilege scoping, WAF at the edge
  • Multi-AZ resilience, point-in-time recovery, tested disaster recovery
05

Managed Web Hosting & Digital Operations

A public-facing site run as infrastructure rather than as a brochure: hosted, patched, monitored, measured, and reported on against a service level. For public bodies that also means accessibility conformance and a content pipeline that keeps producing after the launch.

Scoped for a Virginia community services board: three-year managed web engagement across seven service locations.

  • Auto-scaling container hosting, CDN, auto-renewing TLS, WAF
  • Daily backups with point-in-time recovery and an annual DR test
  • Documented patch cadence for CMS core, plugins, and themes
  • Section 508 and WCAG 2.1 AA conformance, audited and remediated
  • Technical and local SEO, plus a written SEO governance manual
  • GA4, Search Console, and Tag Manager, in one monthly report
06

Data Engineering, Analytics & Decision Support

Turning the output of everything above into something a person decides on. Extraction and classification over unstructured data, cost and risk estimation engines, and dashboards that report to operators and to leadership with a single version of the number.

  • Pipelines from source documents to a queryable, versioned record
  • Deterministic cost and risk estimation engines
  • Operational dashboards: throughput, cycle time, exception aging
  • Executive reporting, and a documented API or export for your BI tool
  • Cohort and funnel analysis over behavioral data
  • Metric definitions written down, so a number means one thing
How we deliver

Discovery is where the price gets honest.

A phased engagement with a decision point at the end of each phase. The scope is settled in the first weeks, and the estimate you sign is confirmed against your real volumes before any build starts.

6phases, each ending in a decision you make
16–20weeks from discovery to cut-over
60days of stabilization with daily monitoring and named exit criteria
5business days’ notice before any change to an integration, workflow or schema

  1. Discovery & gap analysis

    Stakeholder workshops, current-state documentation, and a future-state design you review before anything is configured. Accuracy targets and success metrics are agreed here, in writing.

    2–3 weeks

  2. Design & configuration

    Rules, routing, tolerances, and integration points designed against that future state, since translating today’s manual process into software is how you automate a workaround.

    3–4 weeks

  3. Build & integrate

    Built in non-production, in code, under version control. Separate development, testing, and production environments, provisioned identically, with reviewed promotion between them.

    5–6 weeks

  4. Test & validate

    User acceptance on your real, de-identified data. Accuracy tuning against the targets set in Discovery, then a go/no-go readiness review that you chair.

    3–4 weeks

  5. Train, cut over, stabilize

    Role-based training with recorded sessions, written SOPs specific to your configuration, then a stabilization period with daily monitoring and named exit criteria rather than an open-ended tail.

    3 weeks + 60 days

  6. Operate

    Support against the service levels below, a published release cadence, and a review cycle that retunes thresholds against what the last quarter actually did.

    Ongoing

Every change reaches production through your review

Anything touching an integration, an approval workflow or a data schema is communicated five business days ahead and needs your approval before it deploys. Backward-compatible updates ship on a published cadence with notice. Every production change goes through the pipeline, since that is where it gets tested.

Service levels

What we commit to in writing.

Baseline targets for a managed engagement. They are negotiable upward and they go in the contract, because a service level becomes a commitment only when it is signed.

P1, Critical System down; the process is halted. 15-minute response, 24×7, continuous effort until resolved or a workaround is in place.
P2, High A major function impaired, such as a failing integration. 1-hour response, resolution targeted within one business day.
P3, Medium Non-blocking defect or degraded accuracy on a subset. One business day to respond, five to resolve.
P4, Low Cosmetic, question, or enhancement. Two business days to respond; scheduled into the next release.
Availability 99.9% monthly for hosted services, measured and reported, with maintenance windows announced 72 hours ahead.
Reporting Monthly by the tenth business day, availability, incidents by priority, backups verified, and the metrics that matter to your process. Quarterly strategic review.
Support hours 24×7 on-call for P1. P2–P4 Monday to Friday, 8:00 AM–6:00 PM ET.
How to buy it

Four ways we contract.

Small business, SAM.gov registered, Virginia SWaM certified. We prime engagements sized for a two-person senior team and subcontract on larger scope, and we tell you which before you bid. The first increment is sized to fit inside a quarter, baselined before we build.

Fixed-price, phased

A firm price per phase against a scope agreed in Discovery, the total confirmed against your real volumes before design or build begins. Year-one hosting, processing and support are separate lines, so you can see what recurs.

  • Priced per phase, with a go/no-go between
  • Recurring costs itemized up front

Managed-service retainer

A monthly or annual figure for operating something continuously: hosting, monitoring, content, support against the service levels above, and the reporting that proves it. Multi-year terms with an annual re-confirmation against actual volume.

  • Service levels in the contract, reported monthly
  • Re-confirmed against real volume each year

Time and materials

A four-role rate card, project lead and solution architect, data and AI lead, integration engineer, QA and validation, for change orders, out-of-scope work and advisory that suits a flexible scope.

  • Published rates, senior-weighted
  • Specialist support disclosed and billed explicitly

Subcontract & teaming

As a small-business subcontractor to a prime, or teamed on a bid where we bring the AI, data, or platform scope. NDAs, MSAs, and teaming agreements welcome. SBIR and STTR paths considered.

  • Small · micro · women-owned · minority-owned, Virginia SWaM #828797
  • UEI D9WZWV4JMR33 · CAGE 9KE77
What we work in

The stack we actually operate.

Only tools our principals have run in production, under load, with a pager attached.

Cloud & platform

AWSGoogle Cloud GovCloudKubernetes TerraformDocker ECS & EKSAurora & RDS CloudFrontVercel

Delivery & operations

GitHub ActionsArgoCD SpinnakerHelm GrafanaSplunk ELKPagerDuty

AI & data

Tool-calling agentsHybrid retrieval Vector searchOCR & document AI Multimodal intakeEvaluation harnesses PythonPostgreSQL

Frameworks we build to

NIST AI RMF 1.0NIST SP 800-53 Rev 5 SOC 2 TSCHIPAA Security & Privacy Section 508WCAG 2.1 AA