Data engineering / Applied AI / Automation
Make the work
between systems
flow better.
Connect scattered data, make knowledge useful and automate repetitive handoffs. Bring in a specialist or a coordinated consulting team to build the data foundations and workflows your operations need.
Brazil-based consultancy · English & Portuguese · Remote delivery
A design principle, not a live customer workflow.
Data / AI / Automation
Connect the data.
Improve the workflow.
Bring the right expertise to the part that needs work—from reliable data pipelines to AI-assisted decisions and the systems that act on them.
Data your teams can build on.
Connect operational sources, automate ingestion and build tested warehouse models for analytics and downstream applications.
What the work can include
Full and incremental ingestion, ETL/ELT pipelines, orchestration, dbt models and tests, warehouse transformations, query optimization and operating documentation.
Useful AI with inspectable evidence.
Turn documentation and incoming messages into grounded answers or structured proposals, with validation and human review.
What the work can include
Hybrid retrieval, source citations, structured extraction, evaluation cases and deterministic checks around model output.
Connect the steps between systems.
Build APIs and workflows that move information, collect decisions and keep exceptions visible.
What the work can include
API and mailbox integrations, reconciliation services, spreadsheet transformations, containerized applications and automated delivery pipelines.
Make change verifiable.
Assess data and configuration migrations, investigate mismatches and support systems with clear evidence and handover.
What the work can include
Source-to-target analysis, dry runs, independent validation, data-quality checks, query tuning and runbooks for ongoing operation.
Python · SQL · PostgreSQL · SQL Server · Oracle
BigQuery · dbt · PySpark · Databricks
Airflow · GKE / Kubernetes · Cloud Run · Docker · Helm · CI/CD
The right expertise for the scope
One specialist.
Or a coordinated team.
Boost brings together consultants across data engineering, applied AI and automation. Engage a specialist for a focused assignment, or combine disciplines for a broader delivery.
A specialist in your team
Add focused expertise to an existing team, backlog or delivery plan.
- Data pipelines and warehouse modeling
- AI retrieval and workflow engineering
- Integrations, troubleshooting and migration verification
Best fit: a defined skills gap or an ongoing workstream.
A team around your project
Combine complementary consultants around one scope and an agreed delivery lead.
- Connect sources and establish the data foundation
- Build the AI or automation workflow on top
- Integrate, validate and hand over the result
Best fit: work that crosses data, applications and operations.
We agree the consultants, responsibilities and availability before work starts. Engagements can be project-based or ongoing, with remote collaboration in English and Portuguese.
Find the right delivery setupSelected project experience
Complex problems.
Considered engineering.
Selected work from the consultants behind Boost, spanning data platforms, applied AI, integrations and migration engineering.
Professional experience of our consultants, including work within wider delivery teams. Client identities and proprietary details are omitted. Diagrams show simplified concepts; each story describes its scope and delivery stage.
An assistant that starts
with the documentation.
Engineers needed reliable answers about a complex platform. Plausible API suggestions were not enough: they needed to inspect the source.
- Hybrid retrieval
- BM25 + embeddings
- Source citations
Documentation ingestion and retrieval tooling, a portable operating guide, and technical enablement for colleagues.
Explore the engineering
Technical contribution
Built the ingestion and retrieval tools, using heading-aware chunks and stable source anchors. Combined keyword and semantic search in a command-line interface with ranking explanations.
The important decision
Exact identifiers and natural-language questions need different retrieval strengths. Combining both preserved precise lookups while supporting questions phrased in everyday language.
Making it usable
A retrieve-before-answer practice, inspectable citations, a workshop and a portability guide helped make the tooling usable beyond a single machine.
Delivery boundary
Internal tooling and enablement were delivered. No comparative accuracy score or measured reduction in unsupported answers is claimed here.
Discuss a knowledge-assistant projectFrom payment messages
to a reviewable decision.
A finance workflow relied on reading incoming messages, finding references and checking candidate matches before deciding the next action.
- Structured LLM output
- OAuth / APIs
- Human review
An end-to-end synthetic path from inbound message to validated extraction, candidate matching and a recorded review decision.
Explore the engineering
Technical contribution
Owned the technical design and build: normalized intake, prompt construction, response normalization, deterministic validation, API integration, a review dashboard and decision handling.
The important decision
Business routing stayed in code. The model proposed fields; it did not choose external actions. Reviewers could inspect the extracted information and matching evidence before recording a decision.
Evidence and handover
Synthetic cases exercised the demonstration. The handover documented the process and the remaining work, including persistence, attachment handling and data-source hardening.
Delivery boundary
The final ERP confirmation was a stub. This was a demonstrated proof of concept, with no claim of production ledger posting or measured financial savings.
Discuss a document-intake pilotMigration progress
you can substantiate.
A legacy scheduler encoded calendars differently from its replacement. Copying definitions could not establish that the schedules meant the same thing.
- Python / XML
- Code generation
- Independent validation
Analysis, generation and validation tooling, evidence-backed progress records, and a runbook used by colleagues for later stages.
Explore the engineering
Technical contribution
Parsed source exports, decoded recurrence definitions and classified items before generating target configuration in bounded stages. Built validators, a registry of verified work and coverage reports.
The important decision
Generation intent was not counted as delivery. Progress came from verified records, while ambiguous mappings remained explicit rather than being guessed into a completed tally.
Operational continuity
Dry runs, documented dispositions and a written runbook made the process resumable. Colleagues could run later stages with the same evidence standard.
Delivery boundary
The documented workstream was still in progress. The outcome described here is the tooling and verified stages, not completion of the entire migration.
Discuss migration verificationLet reviewers work
from their inbox.
External reviewers needed to confirm or correct master data without platform accounts. Returned spreadsheets created a manual reconciliation task.
- Email integration
- Spreadsheet transforms
- Traceable merges
A working correction-and-merge flow, a separate test package and an additive production candidate with execution evidence.
Explore the engineering
Technical contribution
Designed and built an extension that sent review workbooks, handled inbound reactions and corrected attachments, transformed returned data and merged corrections with integrity tags.
The important decision
Extend the existing process additively. The package introduced new objects rather than overwriting existing production objects, keeping the change bounded for promotion.
Evidence and handover
A separate test package exercised the correction path end to end. Execution evidence, configuration findings and a peer handover accompanied the production candidate.
Delivery boundary
The documented result is a tested production candidate. Production rollout and adoption are not claimed.
Discuss a review workflowFrom operational databases
to useful analytics.
Operational data needed a repeatable route into an analytical warehouse, with transformations, execution history and orchestration treated as parts of one platform.
- Python / SQL
- BigQuery / dbt
- Airflow / GKE
- Cloud Run / Helm
Configuration-driven ingestion, layered warehouse models, dbt tests and documentation, and orchestrated data workflows.
Explore the engineering
The platform approach
Table-level configuration drives full and incremental Python ingestion. Scheduled workloads run on Kubernetes, with credentials supplied through a managed secret store and execution history written to the analytical warehouse.
From raw records to usable models
dbt structures transformations into staging, warehouse and data-mart layers. Models, macros, tests and generated documentation keep the analytical logic versioned and easier to maintain.
Orchestration and delivery
Airflow coordinates data jobs and invokes containerized dbt workloads on Cloud Run. Container builds, automated checks and deployment pipelines support repeatable delivery.
Experience represented
This narrative summarizes related ingestion, transformation and orchestration implementations from consultant project experience. It describes scheduled full and incremental loading; it does not claim continuous streaming or a measured latency guarantee.
Discuss a data-platform projectBring sales and transaction
records into the same view.
Sales records and financial transactions arrived through different channels. A reconciliation service brought collection, rule processing and warehouse access into one application.
- Python / Flask
- BigQuery
- Docker / Cloud Run
- CI/CD
A reconciliation API with file ingestion, web collection, rule processing and BigQuery persistence, supported by an automated deployment pipeline.
Explore the engineering
The application boundary
A Flask service exposes REST access while handling spreadsheet and CSV inputs. A browser-based collection component brings portal data into the same processing flow.
Processing and storage
An explicit rules engine reconciles sales and financial transaction records. BigQuery provides persistence and query access for the service’s data consumers.
Repeatable deployment
The application is packaged with Docker and Gunicorn for Cloud Run. Its pipeline includes tests, linting, secret scanning, code-quality checks, a container build and deployment.
Experience represented
This is a consultant experience narrative about an implemented service and deployment configuration. Client-specific matching rules, transaction data and unmeasured business outcomes are omitted.
Discuss reconciliation or data integrationSee the checking principle in action.
The independent example below uses fictional data to make a failure visible.
A small piece of proof
Would you trust
this report?
Try a deliberately small engineering example. Matching row counts is not enough: the records and their values need to match, too.
Start with Fresh & complete for a passing check. The other three buttons deliberately introduce a problem: a warning means the check caught it. These are simulated report issues, not website errors. The reference date is fixed at 27 September 2026 so results are reproducible.
Interactive checks require JavaScript. The table below is a static synthetic example; no check has run yet.
Source records
| Asset | Inspection |
|---|---|
| EQ-A | Due |
| EQ-B | Scheduled |
| EQ-C | Complete |
| EQ-D | Due |
Rendered report
| Asset | Inspection |
|---|---|
| EQ-A | Due |
| EQ-B | Scheduled |
| EQ-C | Complete |
| EQ-D | Due |
Load the interactive checks to inspect this fixture.
Independent demonstration, not a customer system. This checks a small set of report invariants; it has no backend, scheduled ingestion or authentication. It does not use an AI model.
Delivery approach
Useful after
the demo is over.
A workflow needs an owner, a way to handle failure and an acceptance plan. Those belong in the design from the beginning.
Define the boundary
One process, agreed inputs and explicit human decisions.
Make it inspectable
Evidence, visible state and reasons for an exception.
Test the failure
Invalid inputs, repeated actions and unavailable systems.
Hand over clearly
Operating steps, remaining limits and maintenance ownership.
A practical starting point
One workflow.
A clear next step.
A Data & Workflow Feasibility Sprint maps the sources and process, tests assumptions and defines what an implementation needs to prove.
Discuss a feasibility sprint ↗Leave with a decision you can use.
- A data-source and process map with integration boundaries.
- Risks, exceptions and required human controls.
- A build, buy or no-build recommendation.
- A bounded pilot scope with acceptance criteria.
Implementation and ongoing support are scoped separately. We propose the specialist mix and delivery responsibilities around the agreed scope.
Let’s look at the workflow
What arrives, where does it go,
and what still needs a person?
Tell us about the data or workflow challenge, the systems involved, and whether you need a specialist in your team or a team to deliver a project. Remote collaboration in English and Portuguese.
Please share a process description first. No confidential documents or credentials are needed for an initial conversation.