One customer, several versions
CRM, finance and support hold different records. Connecting the information depends on manual checks.
Data for Decisions · Tech Human
Your AI needs data your business can trust.
Scattered data, inconsistent records and missing context limit business decisions and AI projects. Tech Human builds the foundation: centralizing access, integrating sources, addressing quality and defining how each dataset can be used.
Senior specialists in data, architecture and artificial intelligence.
The problem starts before the model
Your business collects data. Can it trust what that data says?
When every department works from a different version of reality, leaders must reconcile figures before making decisions. Connecting AI to this environment can carry those inconsistencies into answers, recommendations and automated actions.
The metric changes with the meeting
Sales and finance call different figures revenue, using different rules and periods. The discussion starts with the spreadsheet.
AI gets whatever it finds
Outdated documents, fields without definitions and restricted information enter the same collection without clear rules for use.
Tech Human addresses these weaknesses at their source and builds a shared foundation with explicit criteria for people, systems and AI.
Architecture explained
From data sources to AI answers.
Scattered data needs more than a connection. Tech Human’s senior specialists design and implement the integration, quality, context and access layers that support decisions and AI.
Each layer has a clear responsibility.
Centralizing access and governance connects sources without requiring everything to live in one database.
From source to use · read from top to bottom
01 · ERP · CRM · APIs · documents · events
Sources
Identify systems, owners, formats and permissions. The source determines how data should be integrated and interpreted.
02 · Bronze · batch · CDC · streaming
Ingestion and recording
Capture data with its origin and version, enabling traceability and reprocessing within retention rules.
03 · Silver · schema · deduplication · normalization · quarantine
Quality and protection
Validate structures, resolve inconsistencies and isolate exceptions. Sanitize data according to purpose and sensitivity, with access controls.
04 · Gold · semantics · metrics · data products
Business context
Define shared concepts, relationships and indicators. Deliver data with meaning, ownership and usage criteria.
05 · BI · machine learning · RAG · agents
Use by people and AI
Serve suitable data to each application, preserving context, permissions and evaluation of outcomes.
How AI retrieves authorized knowledge.
RAG retrieves external information to build the answer’s context. Updating the index does not require retraining the model after each change.
Preparing knowledge
01 · Parsing · OCR · sanitization
Prepare documents
Extract content, address quality and apply protection appropriate to its purpose before indexing.
02 · Chunking · source · version · ACL
Segment with context
Split content into useful passages while preserving references to the source, version and permissions.
03 · Embeddings · keyword index
Index
Prepare semantic and keyword search. Vectors also require protection: embeddings are not anonymization.
For each question
01 · Authenticated identity · ACL filters
Ask with identity
Derive permissions from the requester’s trusted identity, without accepting prompt instructions as authorization.
02 · Hybrid search · reranking
Retrieve and rank
Combine keyword and vector search, prioritize relevance and provide only authorized passages to the model.
03 · LLM · context · references
Generate with evidence
Compose the answer with verifiable sources. Agent actions require separate authorization for each tool.
The index supports retrieval within each query’s access permissions.
Six concepts for reading the architecture
- CDC
- Capturing source changes to update downstream systems.
- Schema
- A contract defining data structure, types and fields.
- Lineage
- A record of data origins and transformations.
- Chunking
- Splitting content into passages for retrieval.
- Embeddings
- Vectors that enable comparison of semantic similarity.
- RAG
- Generating answers supported by retrieved content.
Technical references · accessed 24 Sep 2026
What Tech Human delivers
A data foundation that supports your operation.
We connect business decisions to data engineering. Our work covers five areas, tailored to your company's sources, risks and maturity.
Centralize to establish a shared view
We map where data lives, who owns it and which source informs each decision. We organize access and definitions by business domain, without assuming everything must move into one database.
In practice
A map of sources, owners, definitions and access architecture.
Integrate to keep information connected
We connect systems, spreadsheets and documents with update rules, validation and error handling. Refresh frequency follows operational needs; monitoring reveals when a flow stops or delivers incomplete data.
In practice
Implemented integrations, refresh routines and failure alerts.
Clean to improve quality
We address duplicates, incompatible formats, gaps and discrepancies using criteria agreed with the business. We preserve provenance and route exceptions for review, helping prevent automated cleaning from removing legitimate information.
In practice
Quality rules, transformation records and exception handling.
Sanitize to prepare data for its purpose
We prepare datasets according to purpose, sensitivity and permissions. We apply minimization, masking or pseudonymization where appropriate and define what can be made available to analytics, models and agents.
In practice
Datasets prepared for each use, with documented restrictions and criteria.
Govern and make data available with context
We establish definitions, ownership, permissions and traceability. We make data available to metrics, products and AI with known sources, freshness and limitations, alongside routines to track quality over time.
In practice
A catalog, access rules, quality metrics and ways to consume the data.
Data in the age of artificial intelligence
Before connecting AI, you need to account for the data.
An assistant needs to find relevant information. An agent that takes action must also respect rules and permissions. We prepare the foundation around four questions that guide implementation.
Well-prepared data supports AI evaluation. Reliability also requires testing responses, setting limits on actions and maintaining oversight proportionate to risk.
Where did it come from?
We identify the source, owner and transformations so information can be checked and discrepancies investigated.
Is it still current?
We define refresh frequency, validity and version handling to reduce the use of superseded information.
What does it mean in this context?
We organize definitions, relationships and business rules so similar terms are not treated as equivalent. We prepare documents and knowledge bases while preserving the context AI retrieval needs.
Who can use it, and for what?
We carry permissions and purpose restrictions through to use by people, applications and agents.
A hypothetical scenario
“Which customers need our attention?”
Imagine a company that wants AI to support its sales team. Answering this question requires connecting customer records, payments and support interactions through shared criteria.
- 01
Connect the context
Reconcile identities across systems, preserve histories and distinguish current information from older records.
- 02
Define the criteria
Agree with the business on what warrants attention and which data supports that assessment.
- 03
Control how it is used
Show relevant evidence to authorized staff and define which actions require human approval.
This is the path we structure: from fragmented information to a specific, verifiable use that can be monitored. The scenario illustrates the approach; it is not a client result.
How we work
From a decision that matters to a working data foundation.
Tech Human combines assessment, design and implementation. We prioritize a concrete problem and expand against criteria agreed with your team.
Assess and prioritize
We examine decisions, sources, quality, access and dependencies. We define the initial use case, risks and how to verify delivery.
What your company receives
An assessment, priorities and a scope with acceptance criteria.
Build and put it to work
We implement integrations, data treatments and controls. We validate with users and data owners before extending use.
What your company receives
An implemented foundation, recorded validations and operational documentation.
Monitor and evolve
We prepare your team and define monitoring, responsibilities and incident response. Ongoing support from Tech Human can be included in the contracted scope.
What your company receives
Knowledge transfer, an improvement plan and support within the agreed scope.
Senior expertise applied to your business
Specialists who connect business, engineering and accountability.
Our work combines technical leadership and specialist execution. Decisions involve the people who lead, operate and are accountable for the data.
Judgment before tools
We assess the existing environment, constraints and the cost of maintaining each choice before recommending technology.
Depth to deliver
We work across architecture, engineering, quality and governance, connecting technical direction with implementation.
Continuity for your team
We document decisions, rules and responsibilities so your company can track delivery and operate with clarity.
Questions before getting started
Do we need to replace our systems?
Not as a starting point. We assess what can be integrated and retained. We recommend migrations when the problem, risks and existing limitations justify them.
Must we organize all our data before using AI?
We start with the data needed for a defined use case. This allows quality, access and usefulness to be validated before expanding coverage.
What is the difference between cleaning and sanitizing data?
In this service, cleaning addresses quality and consistency. Sanitizing prepares data for an authorized purpose and audience, reducing exposure of unnecessary or sensitive information.
Does Data for Decisions include dashboards and AI?
It can include metrics, dashboards and data preparation for AI applications. We define these deliverables in the scope based on the decision or process that needs to improve.
Does Tech Human also handle implementation?
Yes. Our work can include assessment, architecture, implementation, validation and ongoing support. The proposal sets out deliverables, responsibilities and follow-up for each contracted stage.
Talk to Tech Human
Which decision is waiting for better data?
Bring us your operational challenge. Our specialists help identify what needs to be connected, prepared and governed to move decisions and AI forward.
We start with your context, your sources and the decision that matters.