YOUR CRM KNOWS MORE THAN YOU THINK — YOU JUST CAN'T SEARCH IT YET

THE HIDDEN COST OF UNSEARCHABLE BUSINESS DATA

Every company running Dynamics 365 or Dataverse sits on a goldmine of

operational knowledge: client histories, project records, service cases,

contracts, and contact networks built over years. Yet when a new email lands

in the inbox — a prospect asking about a service, a supplier referencing an

old order, a partner mentioning a company name — someone has to manually dig

through records to figure out: Do we know this person? Have we worked with

their company? Is there an open deal?

That manual lookup costs real time. Multiply it across dozens of daily

inquiries and you're looking at hours of lost productivity per week — not

because the data doesn't exist, but because your CRM was never designed to

understand questions the way people ask them.

There's a better approach. What if your CRM data could be transformed into

searchable, AI-readable representations — compact summaries that capture full

business context and can be matched against incoming information using meaning,

not just keywords? This concept is called custom indexing with data portraits.

WHAT ARE DATA PORTRAITS?

Think of a data portrait as a business card on steroids. Instead of storing

raw database fields, you create a curated summary of each record that captures

the full context of who or what it represents.

Take a client record. In most CRM systems, the information is scattered: the

account name sits in one table, the primary contact in another, the industry

classification in a picklist, active projects in a related entity, the account

manager in yet another lookup. Individually, these are just fields. Together,

they tell a story:

"Contoso Ltd is a mid-size manufacturing company based in Vilnius, managed

by Jonas Petrauskas, with two active automation projects and a pending

renewal worth €45,000."

A data portrait assembles that story automatically by pulling fields across

related entities, flattening them into a single searchable document, and

enriching it with normalized names, keywords, and semantic embeddings. The

result is a document that AI can reason over — not just match against.

The key insight: you decide what the portrait contains. Different business

needs require different portraits of the same data. A sales team might want

accounts indexed with revenue, industry, and deal stage. A support team might

need the same accounts indexed with open cases, SLA status, and escalation

history. Same source data, completely different intelligence layer.

THE THEORY: FROM DATABASE ROWS TO SEARCHABLE INTELLIGENCE

The concept works through a fundamental transformation of how business data is

represented for search:

STEP 1: DEFINE BUSINESS CONTEXT

Rather than searching raw database fields, you define what information matters

for each business scenario. For client matching, that might include company

name, industry, location, key contacts, and relationship status. For project

tracking, it might be project scope, team members, deadlines, and deliverables.

The transformation pulls information from across related entities — following

the relationships your CRM already has — to create a complete picture. If a

contact belongs to an account, and that account has a parent company, all that

context flows into the portrait automatically.

STEP 2: SEMANTIC ENRICHMENT

The portrait isn't just copied data. It's enriched with multiple layers of

searchability:

- Normalized variations of names (handling abbreviations, legal entities,

special characters)

- Keywords extracted from key fields for exact matching

- AI-generated summaries combining all fields into natural language

- Mathematical representations (vectors) that capture meaning

- Key phrases that describe topics and themes

This multi-layered approach means the same portrait can be found through exact

name matching, fuzzy similarity, semantic understanding, or conceptual

relevance.

STEP 3: INTELLIGENT INFRASTRUCTURE

Modern cloud platforms provide the building blocks needed: storage for

documents, search indexes optimized for both keywords and meaning, AI models

that understand language, and embedding models that convert text to mathematical

representations.

The key is orchestration — automatically keeping portraits in sync with source

data, handling updates and deletions, and routing queries to the right indexes

with the right context.

STEP 4: NATURAL LANGUAGE QUERYING

With portraits indexed, queries can be asked the way humans think:

- "Which of our clients works in automotive manufacturing near Munich?"

- "Do we have a contact named something like Petersen at a logistics company?"

- "Find accounts similar to this prospect's profile."

The search system combines vector similarity (understanding meaning) with

keyword matching (catching exact names) and semantic ranking (prioritizing the

most relevant results). It doesn't just find exact matches — it understands

intent.

BUSINESS CASE: MATCHING INCOMING EMAIL INQUIRIES TO CRM DATA

THE SCENARIO

A mid-size B2B services company receives 50–100 email inquiries per day

through various channels: the website contact form, direct emails to sales,

partnership requests, and support questions. Each inquiry needs to be matched

against existing CRM data to determine:

1. Is this an existing client? → Route to their account manager

2. Is this a known contact at a prospect company? → Connect to the active

opportunity

3. Have we done business with their organization before? → Pull up the history

4. Is this related to an open case or project? → Link to the right context

5. Is this completely new? → Create a lead with whatever enrichment we can

provide

THE PROBLEM TODAY

The intake team manually searches the CRM for each inquiry. An email says

"Hi, I'm Maria from Nordström Logistics" — but the CRM has the account listed

as "AB Nordström Logistics Group" and Maria's contact record shows

"M. Nordström-Kvist" because that's how she was entered two years ago.

A keyword search for "Maria Nordström Logistics" returns nothing. The intake

person tries "Nordström," gets 12 results across contacts and accounts, opens

each one, and eventually finds the match — or doesn't, and creates a duplicate.

Time per inquiry: 3–8 minutes for known contacts, 5–15 minutes for ambiguous

ones.

Hidden costs:

- Duplicated contacts and accounts (data quality degradation)

- Inquiries routed to wrong teams (delayed response → lost deals)

- Account managers not notified about their client's inquiry (relationship

damage)

- New leads created without linking to existing company history (missed

context)

THE SOLUTION WITH DATA PORTRAITS

Imagine if the company created three complementary portrait indexes:

ACCOUNTS — Each account portrait contains company name, industry, location,

account manager, website, parent company relationships, revenue tier, and

relationship type. The system automatically generates normalized versions:

"Nordström Logistics," "AB Nordström Logistics Group," "Nordstrom Logistics"

all map to the same entity.

CONTACTS — Each contact portrait includes full name, email, job title, phone,

plus company context pulled from the related account: employer name, industry,

location. Name variations are built in: "Maria Nordström-Kvist," "M. Nordström,"

"Maria Nordstrom" all resolve to the same person.

OPPORTUNITIES — Active deal portraits capture deal name, client, contact,

estimated value, stage, expected close date, and assigned owner. When searching

for company context, relevant opportunities surface automatically.

HOW IT WOULD WORK

When an email arrives, an automated process extracts sender name, email

address, company name, and email content. These become inputs for semantic

searches against the portrait indexes.

Query: "Find contact matching: Maria, email contains nordstrom, works at

logistics company"

The semantic search returns Maria's contact portrait even though her record

says "M. Nordström-Kvist" — because the normalized names field contains

variations without special characters, and the vector embedding understands

that "Maria" and "M." are likely the same person at the same company.

Query: "Find account: Nordström Logistics, industry logistics/transportation"

Returns "AB Nordström Logistics Group" because the normalized name index

strips legal prefixes and suffixes, and the semantic ranking understands that

"Nordström Logistics" and "Nordström Logistics Group" refer to the same entity.

Query: "Active deals with Nordström Logistics"

Returns the open opportunity, giving the intake team immediate context: there's

a €120,000 deal in proposal stage, managed by Erik, expected to close next

month.

THE RESULT

Within seconds (not minutes), the system identifies:

✓ Maria as an existing contact (M. Nordström-Kvist)

✓ Her correct account (AB Nordström Logistics Group)

✓ The active €120K opportunity in proposal stage

✓ Erik as the account manager who should be notified

✓ Full relationship context for whoever responds

MEASURABLE BUSINESS IMPACT

Metric	Before	After	Impact
Time per inquiry	5–10 min	Under 30 sec	90%+ reduction
Duplicate contact creation	~15% of inquiries	Near zero	Clean CRM data
Correct routing on first touch	~60%	~95%	Faster response times
Account manager notification	Manual/inconsistent	Automatic	No missed inquiries
Full context available to responder	Rarely	Always	Higher quality responses

For a team handling 75 inquiries/day, at an average of 7 minutes saved per inquiry, that's nearly 9 hours of productivity recovered daily — the equivalent of a full-time employee doing nothing but CRM lookups.

BEYOND EMAIL: WHERE DATA PORTRAITS CREATE VALUE

Once data is transformed into searchable portraits, the same approach enables

other high-value scenarios:

INCOMING INVOICE MATCHING — An invoice arrives from a vendor. Portrait search

matches it to the correct supplier account, purchase order, and project, even

when naming conventions differ between systems.

RFP RESPONSE ACCELERATION — A new request-for-proposal mentions required

capabilities. Semantic search across project portraits surfaces past projects

with similar scope, giving the proposal team immediate reference material.

REGULATORY COMPLIANCE — A compliance check requires identifying all business

relationships with entities in a specific jurisdiction. Portrait search across

accounts, contacts, and contracts returns results in seconds instead of hours

of manual filtering.

CUSTOMER SUPPORT ESCALATION — A support email doesn't reference a ticket

number. Portrait search matches the sender, their company, and the described

issue to an existing case, preventing duplicate tickets and ensuring continuity.

SALES TERRITORY REASSIGNMENT — When territories change, portrait search can

quickly identify all accounts, contacts, opportunities, and active projects

associated with a region, ensuring nothing falls through the cracks.

MERGER & ACQUISITION DUE DILIGENCE — When evaluating or integrating another

company, portrait search can instantly map relationships, identify overlapping

clients, and surface potential conflicts or synergies.

WHAT MAKES THIS DIFFERENT FROM STANDARD CRM SEARCH

Capability	Standard CRM Search	Custom Data Portraits
Search type	Keyword matching	Semantic understanding + keywords + vectors
Cross-entity	Separate searches per entity	Unified portraits spanning relationships
Fuzzy matching	Limited	Built-in name normalization and similarity
Customizable	Fixed system fields	You choose what goes into each portrait
AI reasoning	None	Language models interpret results in context
Multiple views	One search experience	Different indexes for different teams
Real-time	Depends on sync schedule	Can be near-instant with proper architecture

The fundamental difference is that standard search finds records. Data

portraits find answers.

THE IMPLEMENTATION REALITY

Conceptually, this approach is straightforward. In practice, it requires:

TECHNICAL EXPERTISE — Integrating CRM APIs, cloud search platforms, AI models,

and orchestration logic isn't trivial. You need developers who understand both

business data models and modern AI infrastructure.

CLOUD INFRASTRUCTURE — The storage, search indexes, embedding models, and AI

services typically run in cloud environments (Azure, AWS, or Google Cloud).

Setting up and maintaining this infrastructure requires expertise.

DATA MODELING KNOWLEDGE — Deciding what goes into each portrait type requires

understanding both the business use cases and the underlying CRM data structure.

Poor portrait design yields poor search results.

ONGOING MAINTENANCE — As your CRM schema changes, new entities are added, or

business needs evolve, the portrait definitions and indexes need updates. This

requires continuous attention.

SECURITY & COMPLIANCE — Search results must respect CRM security roles. A

salesperson shouldn't find portraits for accounts they don't have access to.

Implementing this properly requires sophisticated access control.

The good news: the components exist. Modern cloud platforms provide search

services, AI models, and orchestration tools. The challenge is integrating

them with your CRM in a way that's reliable, secure, and maintainable.

IS THIS RIGHT FOR YOUR ORGANIZATION?

Data portraits make the most sense when:

✓ You have significant CRM data with complex relationships across entities

✓ Your team spends considerable time manually searching for matching records

✓ Naming inconsistencies cause duplicates or missed connections

✓ You receive high volumes of external data that needs to be matched (emails,

invoices, forms)

✓ Different teams need different views of the same data

✓ You're already using or planning to use cloud infrastructure

It's probably overkill if:

✗ Your CRM is small and simple (< 1000 accounts)

✗ You rarely need to match external data to CRM records

✗ Standard keyword search meets your needs

✗ You don't have the technical resources to implement and maintain the solution

MOVING FORWARD

The concept of custom indexing with data portraits represents a shift in how

organizations think about their CRM data — from a database of records to a

searchable knowledge base that understands context and meaning.

Implementation requires bridging business needs with technical capabilities.

You need to understand what questions your teams are trying to answer, how

your data is structured, and which cloud services can power the solution.

Some organizations build this internally with development teams. Others work

with specialized consultants who have experience integrating CRM systems with

modern AI infrastructure.

The return on investment comes from time saved, data quality improved, and

opportunities not missed because someone couldn't find the right record fast

enough.

Your data already tells a story. The question is whether you can read it when

it matters.

About This Approach

Custom indexing with data portraits is a design pattern for making enterprise

CRM data searchable using modern AI and cloud infrastructure. While the concept

is general-purpose, implementation requires expertise in Dynamics 365/Dataverse

architecture, cloud services (Azure AI Search, OpenAI, storage), and

integration patterns.

Domestique IT Solutions specializes in implementing these solutions for

organizations using Microsoft Dynamics 365 and Dataverse.