Data na uchanganuzi
Uhandisi wa data na uchanganuzi
Kila mradi wa AI tuliookoa ulishindwa katika tabaka la data, si kwa modeli. Kitengo hiki kipo kuhakikisha hilo halikutokei.
- Sekta
- Huduma
- Unachopata
- 4
Mbinu yetu
Uhandisi wa data na uchanganuzi
We build the load-bearing part of a data-driven organisation: one trustworthy definition of your customers, patients, transactions or assets.
Ingestion, entity resolution across records never designed to agree, and a warehouse analysts and models can both query.
Unachopata
Kila mradi huzalisha nyaraka unazomiliki na unaweza kumpa mhandisi au mkaguzi yeyote.
- Data architecture and modelling
- Batch and streaming pipelines
- Entity resolution and master data
- Quality monitoring, lineage and access control
Matokeo unayopaswa kutarajia
Matokeo unayopaswa kutarajia
- One agreed number for contested metrics
- Reporting without spreadsheet exports
- Data good enough to train models on
Zana na mifumo
Zana na mifumo
- dbt
- Airflow and Dagster
- Spark and Kafka
- Snowflake, BigQuery and Redshift
Tunavyofanya kazi
Tunatoa vipi
Tunatumia michakato bora na mbinu za uendelezaji kama msingi wa kujenga suluhisho za teknolojia za kisasa kwa njia iliyopangwa na yenye mpangilio.
- 01
Planning
We collect the relevant information from you and your stakeholders, including the constraints you have not written down.
- 02
Defining
We document the software needs, agree acceptance criteria, and write down what is out of scope.
- 03
Designing
System and interface documents are prepared against the specification: architecture, data model and stack.
- 04
Building
We implement in short cycles, with a working increment and a visible demonstration at the end of each.
- 05
Testing
We evaluate quality to find and fix defects: automated where possible, exploratory where judgement matters more.
- 06
Deployment
Release runs through an automated pipeline, checked for deployment issues, with a rehearsed rollback.
- 07
Maintenance
We keep the system current, secure and monitored, so you hear about problems from us first.
Maswali
Maswali yanayoulizwa sana
Our data is spread across systems. A blocker?
No. Reconciling those systems is the work.
Do we need a warehouse first?
Usually a modest one, not a grand programme.
How does this relate to AI?
Retrieval, features and evaluation all depend on it.
Huduma zinazohusiana
Huduma zinazohusiana
Sekta zinazohusiana
Tuongee
Hebu tuandae faida yako ijayo.
Tuambie unachotaka kufikia. Utazungumza na mhandisi, si kituo cha simu: kwa kawaida ndani ya dakika chache.
Jumatatu hadi Ijumaa, 08:00 hadi 18:00 Saa za Afrika Mashariki (GMT+3). Msaada wa dharura nje ya saa hizi kwa makubaliano.