Build generative AI systems that survive real users

Engineering support for RAG applications, internal assistants, bounded agent workflows, model gateways, evaluation pipelines, enterprise data connectors, and production rollout.

For teams that need secure data access, measurable output quality, and a credible path from pilot to production.

Scope a GenAI build View AI as a Service

On-request / scoped service

Generative AI Engineering is scoped around your data sources, workflow boundaries, model integrations, evaluation needs, rollout plan, and operating responsibilities.

View scope info

RAG that respects enterprise data

Connect knowledge sources with permission-aware ingestion, hybrid retrieval, citations, freshness controls, and feedback loops.

Agent workflows with guardrails

Design multi-step workflows with durable state, tool policies, human approvals, traces, and clear failure handling.

Evaluation before rollout

Measure relevance, groundedness, safety, latency, and cost with datasets, regression checks, dashboards, and release gates.

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 builds we take onRAG implementationAgentic workflow engineeringModel and platform integration

Generative AI Engineering is for teams that have moved beyond prompt experiments and need reliable software around AI capabilities. Assistance helps define the architecture, connect approved data, integrate models and tools, evaluate behavior, and roll out production workflows without hiding risk behind a demo.

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 builds we take on

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

Build type	What we deliver	Feasibility checks before build
Knowledge assistant / RAG	Source ingestion, retrieval, citations, permissions, evals, UI/API integration, deployment, and runbooks	Source ownership, freshness, access control, expected questions, and acceptance criteria
Internal assistant	Workflow-specific assistant connected to approved systems and feedback loops	User journey, escalation path, data sensitivity, and support ownership
Document-processing workflow	Extraction, classification, validation, exception routing, and human review	Document variance, target schema, error tolerance, and review process
Agentic workflow pilot	Tool policies, orchestration, state, approval checkpoints, traces, and pilot rollout	Side-effect risk, tool authentication, rollback path, and business owner
LLMOps foundation	Provider abstraction, prompt/config versioning, cost/quality dashboards, eval runner, and release gates	Number of workflows, model constraints, budget model, and operations owner

Operating modelSection 02

RAG implementation

Responsibilities, response paths, and technical changes are made explicit before work starts.

Retrieval-augmented generation only works when the retrieval layer is engineered around the actual data estate. We build RAG systems that make source ownership, permissions, freshness, and answer quality explicit.

Implementation focus

Included work

inventory data sources and classify sensitivity, ownership, and update frequency
design ingestion, normalization, chunking, metadata, and indexing paths
combine vector, keyword, metadata, and graph-style retrieval where useful
enforce user and group permissions before context reaches the model
add citations, source previews, confidence signals, and feedback capture
test retrieval relevance with representative questions and expected sources

Implementation focus

Common data sources

Source type	Engineering considerations
Document stores	Versioning, metadata, access control, duplicate content, and document lifecycle
Databases	Query safety, row-level permissions, schema context, and generated SQL review
Ticketing and CRM tools	Tenant boundaries, field-level sensitivity, workflow context, and rate limits
Code repositories	Branch selection, secrets redaction, dependency context, and repository permissions
Search platforms	Hybrid ranking, freshness, index quality, and source attribution

OutcomeSection 03

Agentic workflow engineering

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

Agents are useful when they can complete constrained work with tools, state, and oversight. We help teams choose where an agent should act, where it should ask for approval, and where a deterministic workflow is safer.

Typical patterns:

support triage that reads context, classifies requests, and drafts next actions
operations assistants that inspect approved signals and prepare incident summaries
document workflows that extract fields, compare records, and route exceptions
research workflows that gather sources and produce reviewable artifacts
developer-assistance workflows that inspect repositories and prepare bounded change proposals

Production controls:

explicit tool allowlists and deny rules
timeout, retry, and budget limits per workflow
human approval before external side effects
traceability for prompts, tool calls, model responses, retrieval, and state changes
rollback or compensation steps for supported actions

EvidenceSection 04

Model and platform integration

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

We do not assume one model or provider is right for every use case. The platform layer should make model choice visible, testable, and changeable.

Platform concern	Assistance approach
Provider selection	Compare providers by task, latency, cost, data policy, availability, and quality target
Prompt management	Version prompts and configuration, document changes, and compare behavior across releases
Cost control	Track spend by user, workflow, model, and environment with budget alerts
Reliability	Add retries and health checks where safe; avoid pretending providers are behaviorally interchangeable
Deployment	Use cloud, single-tenant, EU-resident, or self-hosted patterns based on data and risk needs
Security	Keep secrets, credentials, and sensitive data outside uncontrolled prompt paths

Operating modelSection 05

Evaluation and quality gates

Responsibilities, response paths, and technical changes are made explicit before work starts.

AI quality cannot be proven with a handful of happy-path prompts. We build evaluation loops before production traffic grows.

What changes

Evaluation dimensions

retrieval relevance — did the system find the right source material?
groundedness — did the answer stay within retrieved or approved context?
task success — did the workflow complete the intended business step?
safety and policy — did the model avoid restricted actions or data exposure?
latency and cost — is the experience usable and economically sustainable?
regression risk — did a prompt, model, tool, or index change break known scenarios?

What changes

Deliverables

Deliverable	Purpose
Evaluation dataset	Representative prompts, workflows, expected sources, and acceptance criteria
Automated eval runner	Repeatable checks in CI, staging, or release review
Production dashboards	Quality, latency, cost, retrieval, and failure signals in one place
Release gates	Clear thresholds for launch, rollback, and manual review
Review cadence	Scheduled analysis of failures, feedback, and model/provider changes

OutcomeSection 06

Enterprise rollout path

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

Discovery and architecture — define the workflow, users, data sources, risk boundaries, and target operating model.
Prototype with real constraints — build a thin slice using representative data, permissions, and evaluation criteria.
Production foundation — add deployment automation, observability, model routing, access controls, and runbooks.
Pilot and hardening — launch to a controlled user group, review failures, refine prompts/retrieval/tools, and tune cost.
Operational handoff — document ownership, support paths, dashboards, incident response, and release procedures.

Next stepSection 07

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

AI as a Service — broader discovery, implementation, and support for AI capabilities
AI Agent Infrastructure — scoped runtimes, gateway, memory, and tool execution for agents
Agent Orchestration — durable coordination for multi-agent and multi-step workflows
Agent Observability — traces, quality signals, and cost visibility for AI systems
Managed MCP Servers — secure hosted tool servers for agents and assistants
Sovereign Cloud — deployment patterns for teams with data residency and control requirements

Next stepSection 08

Getting started

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

Start with a scoping session. We will review the workflow, data sources, user permissions, model constraints, evaluation needs, and the smallest production slice worth building. Scope a generative AI engineering project →

Ready to get started?

Book a quote review or talk to an engineer.