Ship useful AI with production controls

We help teams choose feasible AI use cases, connect approved data, integrate models responsibly, measure quality and cost, and hand off systems your operators can support.

Best for teams that need engineering evidence, not a generic AI transformation deck.

Scope an AI implementation View GenAI engineering

On-request / scoped service

AI as a Service is scoped around the workflow, data sources, model operations, governance requirements, and production support model.

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Discovery before build

Use-case triage, data/source inventory, risk review, cost assumptions, and the smallest credible production slice.

RAG, assistants, and agents

Permission-aware retrieval, workflow orchestration, tool policies, human approvals, and measured rollout.

Operations and governance

Model routing, evals, traces, cost dashboards, data-handling rules, deployment gates, and handoff runbooks.

Service playbook

From problem to operating evidence

Main content is structured like a case study: context first, scoped work next, then the operating changes and evidence a team can use after handoff.

Service briefFeasible service tracksWhat is includedOperating principlesEngagement options

AI as a Service is Assistance's umbrella engagement for practical AI delivery. It is intentionally scoped around your workflow, data, security boundaries, and operating model before implementation starts.

We focus on AI systems that can be inspected, measured, and supported: RAG assistants, internal copilots, bounded agent workflows, LLMOps foundations, AI observability, governed tool access, and AI-ready data paths.

Case-study lens

Scoped

Problem, responsibility, and handoff boundaries before implementation.

Evidence

Dashboards, runbooks, reviews, and operating records over borrowed logos.

Outcomes

Conservative summaries focused on observable operational improvement.

EvidenceSection 01

Feasible service tracks

Runbooks, dashboards, reviews, and handoff material make the work auditable.

Track	Best for	Typical outputs
AI discovery and readiness	Teams with many AI ideas and no delivery sequence	Use-case shortlist, data/source map, risk review, architecture options, cost assumptions, and implementation roadmap
RAG and internal assistants	Support, operations, product, or engineering teams that need answers grounded in approved sources	Ingestion pipeline, retrieval/index strategy, citations, permissions, feedback loop, evaluation dataset, deployment, and runbook
LLMOps foundation	Teams with multiple AI prototypes, provider choices, or unclear spend and release controls	Model gateway or provider abstraction, prompt/config versioning, budgets, eval runner, dashboards, and release gates
Agentic workflow implementation	Teams automating bounded multi-step workflows	Workflow map, tool policies, state/checkpointing, human approvals, traces, dry-run path, and pilot rollout
AI observability and evaluation	Teams operating AI features without enough visibility	Model/tool/retrieval traces, token and cost attribution, quality checks, regression tests, alerts, and review cadence
AI governance for engineering teams	Security and platform teams formalizing AI adoption	Usage inventory, data-handling rules, model/provider policy, approval gates, logging/redaction rules, and audit evidence
AI-ready data platform work	Teams whose data estate blocks reliable AI	Data pipelines, freshness checks, metadata, search/vector indexes, access controls, and ownership documentation

ScopeSection 02

What is included

The work is broken into visible capabilities, acceptance points, and handoff artifacts.

Assessment step

Discovery and solution design

workflow and user-journey discovery
data source inventory, ownership, sensitivity, and freshness review
model-provider and hosting decision record
security, privacy, and tool-access risk review
evaluation plan for quality, groundedness, safety, latency, and cost
implementation plan for the smallest useful production slice

Implementation focus

RAG and assistant implementation

document ingestion, normalization, metadata, and lifecycle rules
vector, keyword, or hybrid search architecture
permission-aware retrieval and citation handling
prompt, tool, and workflow orchestration
human review, escalation, and feedback capture
release checks based on representative questions and expected sources

What changes

Production AI operations

LLM gateway or provider abstraction where it reduces operational risk
prompt and configuration versioning
secrets management, network boundaries, and audit logs
traces for model calls, retrieval, tool use, and workflow state
token budgets, spend dashboards, and model/provider cost reviews
handoff runbooks for incidents, provider changes, and model-behavior regressions

OutcomeSection 03

Operating principles

Expected changes are framed as practical operating improvements, not unsupported guarantees.

customer data boundaries are defined before implementation
AI output quality is measured, not assumed
tools and external side effects are allowlisted and reviewed
sensitive actions use explicit approval paths until the workflow is proven
provider choice is documented and tested per workflow
costs, prompts, tool calls, retrieval, and model behavior are observable
Assistance says no when the data, risk, or operating model is not ready

EvidenceSection 04

Engagement options

Runbooks, dashboards, reviews, and handoff material make the work auditable.

Package	Best for	Typical deliverables
AI Discovery Sprint	Teams validating feasibility and risk	Use-case shortlist, data map, architecture options, risk review, estimate, rollout path
RAG / Assistant Build	Teams ready to implement one production workflow	Retrieval pipeline, app integration, evals, deployment, dashboards, and runbooks
LLMOps Platform Foundation	Teams standardizing multiple AI initiatives	Model gateway, prompt/config versioning, observability, budget controls, release gates, and security guardrails
Agentic Workflow Pilot	Teams with a bounded workflow and clear ownership	Orchestration, tool policies, approval checkpoints, traceability, dry-run mode, and pilot rollout
Ongoing AI Operations	Production AI systems needing support	Monitoring, incident response, quality reviews, provider/cost tuning, and continuous improvement backlog

Next stepSection 05

Decision points and common questions are made explicit so follow-up work is scoped cleanly.

Generative AI Engineering — RAG, assistants, model integration, evaluations, and rollout
AI Agent Infrastructure — scoped runtime, gateway, memory, and tool-execution foundations for agents
Agent Orchestration — durable workflows, handoffs, retries, and human approvals
Agent Observability — traces, evals, cost attribution, and production visibility
Managed MCP Servers — scoped tool servers and connector hosting for agent systems
Data as a Service — data foundations that make AI and analytics reliable
Security Compliance — security and compliance evidence around production systems

Next stepSection 06

Getting started

Decision points and common questions are made explicit so follow-up work is scoped cleanly.

Start with an AI scoping session. We will review the workflow, data sources, security constraints, operating responsibilities, and shortest credible path to production. Scope an AI implementation →

Ready to get started?

Book a quote review or talk to an engineer.

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Pricing

Flexible scopes available. if you need custom terms or bundled service pricing.

On-request scope

Quoted