In the future, AI will augment every step of the data engineering workflow

In the future, AI will augment every step of the data engineering workflow

As LLMs reduce the cost of intelligence, we'll use AI systems to automate the work we don't want, like writing documentation.

As LLMs reduce the cost of intelligence, we'll use AI systems to automate the work we don't want, like writing documentation.

Effortless Docs

Our AI bots silently collaborate, auto-analyzing and generating tech docs for your codebase. Pull requests trigger updates, keeping docs in sync.

Code Assist

Automatically identify bugs and inefficiencies, offer refactoring suggestions, and generate code from natural language.

Interactive Codebase

Query your data, ask questions, and propose improvements to your underlying codebase, data model, and documentation.

Data Accessibility

Need insights? Our AI crafts queries, visualizations, and interprets complex data, revealing hidden insights effortlessly.

Our mission

Our mission

Our mission

To make code/data more accessible

To make code/data more accessible

Archiva streamlines and automates documentation creation and maintenance, ensuring instant code clarity.

Archiva streamlines and automates documentation creation and maintenance, ensuring instant code clarity.

Outcomes

Outcomes

Developer productivity

Free developers from the time-consuming task of writing and updating documentation manually. This allows them to focus more on coding and other high-value activities.

Expedited onboarding

New team members can quickly come up to speed on existing projects, reducing the learning curve and training time required for new developers, facilitating faster integration into teams.

Audit readiness

Ensure that all code changes are documented in real-time, maintain compliance with industry regulations, and makes audit processes more straightforward.

Start automating your documentation and give everyone the context they deserve

Start automating your documentation and give everyone the context they deserve

Start automating your documentation and give everyone the context they deserve

Monthly revenue

1

10

11

12

2

3

4

5

6

7

8

9

Month

0

2,000,000

4,000,000

6,000,000

8,000,000

10,000,000

12,000,000

14,000,000

16,000,000

Revenue

2011

2012

2013

2014

Year

SELECT
EXTRACT
(YEAR FROM orderdate) AS year,
EXTRACT(MONTH FROM orderdate) AS month,
SUM(totaldue) AS revenue
FROM
sales.salesorderheader
GROUP BY
year,
month
ORDER BY
year,
month

year

month

revenue

2011

5

567020.9498

2011

6

507096.4690

2011

7

2292182.8828

2011

8

2800576.1723

2011

9

554791.6082

2011

10

5156269.5291

2011

11

815313.0152

2011

12

1462448.8986

2012

1

4458337.4444

2012

2

1649051.9001

2012

3

3336347.4716

2012

4

1871923.5039

2012

5

3452924.4537

2012

6

4610647.2153

2012

7

3840231.4590

2012

8

2442451.1831

2012

9

3881724.1860

2012

10

2858060.1970

2012

11

2097153.1292

2012

12

3176848.1687

2013

1

2340061.5521

2013

2

2600218.8667

2013

3

3831605.9389

2013

4

2840711.1734

2013

5

3658084.9461

2013

6

5726265.2635

2013

7

5521840.8445

2013

8

3733973.0032

2013

9

5083505.3374

2013

10

5374375.9418

2013

11

3694667.9998

2013

12

4560577.0958

2014

1

4798027.8709

2014

2

1478213.2920

2014

3

8097036.3137

2014

4

1985886.1496

2014

5

6006183.2110

2014

6

54151.4785

Monthly revenue

1

10

11

12

2

3

4

5

6

7

8

9

Month

0

2,000,000

4,000,000

6,000,000

8,000,000

10,000,000

12,000,000

14,000,000

16,000,000

Revenue

2011

2012

2013

2014

Year

SELECT
EXTRACT
(YEAR FROM orderdate) AS year,
EXTRACT(MONTH FROM orderdate) AS month,
SUM(totaldue) AS revenue
FROM
sales.salesorderheader
GROUP BY
year,
month
ORDER BY
year,
month

year

month

revenue

2011

5

567020.9498

2011

6

507096.4690

2011

7

2292182.8828

2011

8

2800576.1723

2011

9

554791.6082

2011

10

5156269.5291

2011

11

815313.0152

2011

12

1462448.8986

2012

1

4458337.4444

2012

2

1649051.9001

2012

3

3336347.4716

2012

4

1871923.5039

2012

5

3452924.4537

2012

6

4610647.2153

2012

7

3840231.4590

2012

8

2442451.1831

2012

9

3881724.1860

2012

10

2858060.1970

2012

11

2097153.1292

2012

12

3176848.1687

2013

1

2340061.5521

2013

2

2600218.8667

2013

3

3831605.9389

2013

4

2840711.1734

2013

5

3658084.9461

2013

6

5726265.2635

2013

7

5521840.8445

2013

8

3733973.0032

2013

9

5083505.3374

2013

10

5374375.9418

2013

11

3694667.9998

2013

12

4560577.0958

2014

1

4798027.8709

2014

2

1478213.2920

2014

3

8097036.3137

2014

4

1985886.1496

2014

5

6006183.2110

2014

6

54151.4785