One Hub,
Many Agents

ORCA helped us with the analysis of financial returns from Unions. It can also generate both PowerPoint slides and Word reports based on the analysis done, which can save us hours of effort.

Very happy with ORCA! Can query the whole analysis end-to-end using natural language... Charts are nice too! No need to code anymore.

I have been using ORCA to support data analytics and investigation work. Reviewing large volumes of data, surfacing anomalies, and generating insights used to take 3-4 hours of manual effort each day. With ORCA, I now save approximately 1-2 hours each day, translating to 25-50% time savings. ORCA’s ability to build and reuse workflows reduced repetitive effort while ensuring a systematic approach to data analysis and enabling me to focus on interpreting findings and developing recommendations. One of the key reasons I chose ORCA over alternatives such as Microsoft Copilot is its range of specialised agents and workflow capabilities. The workflow feature allows me to structure and automate multi-step analytical processes, making it easier to manage complex investigations and repeat analyses consistently. Combined with the agents specialised for government work, I am now able to iterate quickly on analysis, explore hypotheses, and obtain structured outputs that can be readily incorporated into my work.

Orca helped me complete a text analysis task involving hundreds of form responses in one day instead of two. Its guided, conversational workflow feels intuitive and efficient, and the analysis outputs have been smart, practical, and genuinely useful.

What used to take 3-4 hours to manually convert a Word document into a PowerPoint presentation can now be done within 30-45 minutes, complete with all the necessary slides formatted in MOM template. This has saved the team about 2.5 hours per month on average, or up to 85% of time savings, allowing us to redirect our focus to more meaningful work. ORCA has truly streamlined our workflow and made the presentation preparation process significantly more efficient.

What stood out about Orca was how thoughtfully it was designed for public officers and the work that we do. From data analysis to drafting and critiquing presentations, the seamless integration makes it an end-to-end platform for our workflow. Definitely worth trying it out!

MOMA used it to conduct a broad landscape analysis of our HRPS data on employee training, with the aim of getting a topline view of training activity across MOM. ORCA helped us uncover insights on the training landscape, and came out with useful variables such as average training expenditure per officer, training stats broken down by divisions and job functions, and expenditure incurred by training provider. It also allowed us to deep-dive into patterns in hours and money spent on training across the organisation. This gave us a much clearer, data-backed picture and cut down significantly on the time needed for data processing to pull together such a broad-based analysis. Overall, I found ORCA effective for this kind of exploratory, landscape-level work.

ORCA enabled us to perform exploratory text analytics with great efficiency and reliable accuracy. Its ability to orchestrate multiple agents within a single workflow significantly reduced the time needed to transform raw data into preliminary insights — cutting what once took 2-3 days down to 5-6 hours.

ORCA was used to clean manpower and graduate datasets with inconsistent naming conventions. Its Text Analyst agent intelligently clustered similar text entries into standardised categories, handling nuanced wording variations that would have been tedious to resolve manually. This saved approximately 3 days of work (30% time savings), with reusable methodology and code that could be applied across other workstreams. Compared to Pair and Desk, ORCA was notably more capable. Pair could generate code and suggest fixes, but outputs frequently errored and required significant rework. Desk surfaced trends well but lacked technical depth, and its interface often hung during generation. Despite having a larger character limit, Desk also struggled to retain context across lengthy instructions. ORCA's ability to draw on multiple clustering techniques, explain its reasoning, and support iterative discussion made it the most effective tool for analytical work spanning data cleaning, categorisation, and methodology development.

ORCA helped us with the analysis of financial returns from Unions. It can also generate both PowerPoint slides and Word reports based on the analysis done, which can save us hours of effort.

Very happy with ORCA! Can query the whole analysis end-to-end using natural language... Charts are nice too! No need to code anymore.

I have been using ORCA to support data analytics and investigation work. Reviewing large volumes of data, surfacing anomalies, and generating insights used to take 3-4 hours of manual effort each day. With ORCA, I now save approximately 1-2 hours each day, translating to 25-50% time savings. ORCA’s ability to build and reuse workflows reduced repetitive effort while ensuring a systematic approach to data analysis and enabling me to focus on interpreting findings and developing recommendations. One of the key reasons I chose ORCA over alternatives such as Microsoft Copilot is its range of specialised agents and workflow capabilities. The workflow feature allows me to structure and automate multi-step analytical processes, making it easier to manage complex investigations and repeat analyses consistently. Combined with the agents specialised for government work, I am now able to iterate quickly on analysis, explore hypotheses, and obtain structured outputs that can be readily incorporated into my work.

Orca helped me complete a text analysis task involving hundreds of form responses in one day instead of two. Its guided, conversational workflow feels intuitive and efficient, and the analysis outputs have been smart, practical, and genuinely useful.

What used to take 3-4 hours to manually convert a Word document into a PowerPoint presentation can now be done within 30-45 minutes, complete with all the necessary slides formatted in MOM template. This has saved the team about 2.5 hours per month on average, or up to 85% of time savings, allowing us to redirect our focus to more meaningful work. ORCA has truly streamlined our workflow and made the presentation preparation process significantly more efficient.

What stood out about Orca was how thoughtfully it was designed for public officers and the work that we do. From data analysis to drafting and critiquing presentations, the seamless integration makes it an end-to-end platform for our workflow. Definitely worth trying it out!

MOMA used it to conduct a broad landscape analysis of our HRPS data on employee training, with the aim of getting a topline view of training activity across MOM. ORCA helped us uncover insights on the training landscape, and came out with useful variables such as average training expenditure per officer, training stats broken down by divisions and job functions, and expenditure incurred by training provider. It also allowed us to deep-dive into patterns in hours and money spent on training across the organisation. This gave us a much clearer, data-backed picture and cut down significantly on the time needed for data processing to pull together such a broad-based analysis. Overall, I found ORCA effective for this kind of exploratory, landscape-level work.

ORCA enabled us to perform exploratory text analytics with great efficiency and reliable accuracy. Its ability to orchestrate multiple agents within a single workflow significantly reduced the time needed to transform raw data into preliminary insights — cutting what once took 2-3 days down to 5-6 hours.

ORCA was used to clean manpower and graduate datasets with inconsistent naming conventions. Its Text Analyst agent intelligently clustered similar text entries into standardised categories, handling nuanced wording variations that would have been tedious to resolve manually. This saved approximately 3 days of work (30% time savings), with reusable methodology and code that could be applied across other workstreams. Compared to Pair and Desk, ORCA was notably more capable. Pair could generate code and suggest fixes, but outputs frequently errored and required significant rework. Desk surfaced trends well but lacked technical depth, and its interface often hung during generation. Despite having a larger character limit, Desk also struggled to retain context across lengthy instructions. ORCA's ability to draw on multiple clustering techniques, explain its reasoning, and support iterative discussion made it the most effective tool for analytical work spanning data cleaning, categorisation, and methodology development.

ORCA helped us with the analysis of financial returns from Unions. It can also generate both PowerPoint slides and Word reports based on the analysis done, which can save us hours of effort.

Very happy with ORCA! Can query the whole analysis end-to-end using natural language... Charts are nice too! No need to code anymore.

I have been using ORCA to support data analytics and investigation work. Reviewing large volumes of data, surfacing anomalies, and generating insights used to take 3-4 hours of manual effort each day. With ORCA, I now save approximately 1-2 hours each day, translating to 25-50% time savings. ORCA’s ability to build and reuse workflows reduced repetitive effort while ensuring a systematic approach to data analysis and enabling me to focus on interpreting findings and developing recommendations. One of the key reasons I chose ORCA over alternatives such as Microsoft Copilot is its range of specialised agents and workflow capabilities. The workflow feature allows me to structure and automate multi-step analytical processes, making it easier to manage complex investigations and repeat analyses consistently. Combined with the agents specialised for government work, I am now able to iterate quickly on analysis, explore hypotheses, and obtain structured outputs that can be readily incorporated into my work.

Orca helped me complete a text analysis task involving hundreds of form responses in one day instead of two. Its guided, conversational workflow feels intuitive and efficient, and the analysis outputs have been smart, practical, and genuinely useful.

What used to take 3-4 hours to manually convert a Word document into a PowerPoint presentation can now be done within 30-45 minutes, complete with all the necessary slides formatted in MOM template. This has saved the team about 2.5 hours per month on average, or up to 85% of time savings, allowing us to redirect our focus to more meaningful work. ORCA has truly streamlined our workflow and made the presentation preparation process significantly more efficient.

What stood out about Orca was how thoughtfully it was designed for public officers and the work that we do. From data analysis to drafting and critiquing presentations, the seamless integration makes it an end-to-end platform for our workflow. Definitely worth trying it out!

MOMA used it to conduct a broad landscape analysis of our HRPS data on employee training, with the aim of getting a topline view of training activity across MOM. ORCA helped us uncover insights on the training landscape, and came out with useful variables such as average training expenditure per officer, training stats broken down by divisions and job functions, and expenditure incurred by training provider. It also allowed us to deep-dive into patterns in hours and money spent on training across the organisation. This gave us a much clearer, data-backed picture and cut down significantly on the time needed for data processing to pull together such a broad-based analysis. Overall, I found ORCA effective for this kind of exploratory, landscape-level work.

ORCA enabled us to perform exploratory text analytics with great efficiency and reliable accuracy. Its ability to orchestrate multiple agents within a single workflow significantly reduced the time needed to transform raw data into preliminary insights — cutting what once took 2-3 days down to 5-6 hours.

ORCA was used to clean manpower and graduate datasets with inconsistent naming conventions. Its Text Analyst agent intelligently clustered similar text entries into standardised categories, handling nuanced wording variations that would have been tedious to resolve manually. This saved approximately 3 days of work (30% time savings), with reusable methodology and code that could be applied across other workstreams. Compared to Pair and Desk, ORCA was notably more capable. Pair could generate code and suggest fixes, but outputs frequently errored and required significant rework. Desk surfaced trends well but lacked technical depth, and its interface often hung during generation. Despite having a larger character limit, Desk also struggled to retain context across lengthy instructions. ORCA's ability to draw on multiple clustering techniques, explain its reasoning, and support iterative discussion made it the most effective tool for analytical work spanning data cleaning, categorisation, and methodology development.