Implementation services

From audit to live workflow.

Six services span the full implementation lifecycle - audit, build, integration, governance, and post-launch operation. The right entry point depends on the bottleneck, the systems involved, and how tightly the first rollout should be scoped.

Start with the audit

Map the workflow, choose the first build, and make the next implementation step concrete.

Build inside the current stack

Email, CRM, ERP, drive, and internal tools remain the source of truth. Realah integrates around them.

Operate after launch

Monitoring, tuning, review queues, and owner enablement matter as much as the first deployment.

01

AI Opportunity Audit

A short scoping engagement to map how work moves, identify the highest-leverage AI use cases, and produce a prioritized first build with a clear scope, likely timeline, and integration plan.

Workflow inventory

Document the repetitive tasks, handoffs, exception paths, and approval layers across inboxes, documents, CRMs, spreadsheets, and internal systems.

ROI prioritization

Rank candidate workflows by impact, build complexity, data readiness, and operational risk.

Fixed first build

Define what gets automated, what stays human-reviewed, and what systems the first rollout must integrate with.

Request an AI Audit
Operational workflow analysis
Workflow automation systems

02

Workflow Automation

Automate repetitive operational work across the systems the team already uses. AI runs as part of execution - embedded in email, CRM, ERP, and back-office tools - not as another standalone app to manage.

Inbox automation

Classify, route, summarize, and draft responses across leads, support, and operational queues.

CRM updates

Create records, enrich fields, trigger follow-ups, and keep pipeline data clean without manual re-entry.

Approvals & escalations

Add human sign-off where accuracy matters and route exceptions to the right person quickly.

Back-office ops

Quoting, onboarding, reporting, scheduling, and recurring follow-up work automated against current systems.

Shared Inbox Page Back-Office Page

03

Internal Search & Copilots

Turn the documents, SOPs, tickets, contracts, and historical knowledge already inside the business into a queryable layer with cited answers and role-based access. Then connect those answers into downstream workflows.

Search across real sources

Connect drives, wikis, CRMs, help desks, and internal docs into one searchable layer with freshness and citations.

Answers with citations

Every response links back to source material so the team can verify, correct, and trust the system over time.

Role-based access

Search results respect current permissions so sensitive documents stay scoped to authorized users.

Internal Search Page
Internal AI assistant
Document operations workflow

04

Document Operations

Many operational bottlenecks are document bottlenecks. Extract, validate, and route information from invoices, contracts, claims, and shipping documents into systems of record, with human review where confidence is low.

Structured extraction

Pull fields from invoices, contracts, claims, intake forms, and shipment paperwork into structured records ready for downstream systems.

Human review queues

Route low-confidence or high-risk cases to a person before anything is finalized.

System sync

Push validated data into the CRM, ERP, accounting system, ticketing tool, or spreadsheet that owns the workflow.

Invoice Processing Page

05

Integration & Governance

AI runs reliably in production when it is connected to the right systems, gated by clear approval rules, and instrumented with monitoring. Realah handles the integration plumbing and the policy layer around what AI is allowed to do.

System connectors

Email, drive, CRM, ERP, help desk, databases, and bespoke tooling wired into the rollout.

Approval layers

Define what AI decides alone, what requires human review, and what stays manual.

Data & security controls

Sensitive-data handling, prompt guardrails, fallback behavior, and audit logging aligned to current posture.

Operational reporting

Track volume, exceptions, manual saves, and failure modes so the rollout gets better over time.

Design the guardrails
AI governance and systems integration
Team managing live AI workflows

06

Managed AI Ops

Launch is the start. Ongoing monitoring of output quality, prompt and routing tuning as edge cases appear, operator training, and disciplined expansion to the next workflow once the first is proven.

Quality monitoring

Review outputs, track miss rates, and tighten prompts or routing logic as new edge cases surface in production.

Workflow expansion

Once the first automation proves itself, adjacent use cases ship faster on the same integration foundation.

Team enablement

Train operators, document ownership, and keep the rollout stable beyond the launch date.

Plan Managed AI Ops

Workflow pages

Targeted pages for the common first rollout.

These pages are built for search intent and buyer fit. Use them when the problem is already specific.

Invoice Processing Automation

For AP teams, claims, intake forms, and document-heavy operations.

Shared Inbox Automation

For support, sales, and operational mailboxes that need triage and routing.

Internal Search & Copilots

For knowledge retrieval, cited answers, and role-based internal search.

Back-Office Workflow Automation

For quoting, onboarding, reporting, approvals, and cross-system operations.

Not sure which service or workflow fits first?

Start with the AI Opportunity Audit. The first job is to pick the right workflow, not to force every problem into the same AI shape.

Request an AI Audit