AI where it helps.A defined task.A checked result.
AI can help read, organise or prepare information, but its usefulness needs testing in the actual work. SIXIS chooses clearly defined tasks and agrees how outputs are used, who checks them and what happens when they are wrong or incomplete.
AI can help read, organise or prepare information, but its usefulness needs testing in the actual work. SIXIS chooses clearly defined tasks and agrees how outputs are used, who checks them and what happens when they are wrong or incomplete.
Tell us what consumes time See how we workWhere due
utility checked.
AI may be suitable for variable information and training activities. Exact rules, calculations and sensitive decisions may require other mechanisms or human confirmation.
The team reads a lot of similar information.
The requests or documents have different forms, but the colleagues are looking for the same details. It may be useful to prepare a summary, citing the source and flagging missing information, before verifying the responsible person.
A working variant would shorten the preparation.
A colleague always starts from a blank page for a synthesis or an answer. A first result can help if it uses authorized information, remains verifiable, and is not automatically submitted or approved.
There is an idea of AI, but no criterion of success.
The firm wants to use AI, but it is not clear what task it solves and how much verification it will require. Without real cases and an expected result, a convincing demonstration can hide errors that occur in use.
We choose the task.
We test on actual cases.
AI must help a precise activity and leave visible the limits of the result, not create the impression of a certainty that it does not have.
We can begin with a limited test. A rules-based solution or process simplification may be better suited for the same need.
The SIXIS collaboration modelWe define the task and the alternative.
We determine what the colleague is doing today, what result is useful to him and how he checks it. We compare the use of AI with simple rules or reorganizing the activity. We choose a bounded task without promising to automate the entire role or process.
From you Examples of work and the criterion for a useful result to the team.
We check the data and the consequences of errors.
We clarify what information can be used, where it is processed, and who authorizes access. We identify sensitive data and what effect a wrong result would have. We determine what can be prepared as a proposal and what requires verification before use.
From you Authorized sources and persons validating access and level of risk.
We construct a representative test.
We choose ordinary, incomplete and difficult cases. We specify the expected output and the information that the solution does not have to invent. We check to see if it can show the source, limit, or fuzziness, rather than giving an apparently complete answer in every situation.
From you Actual cases that may be used and the evaluation criteria.
We compare results and effort.
We evaluate the correctness, omissions and time required for verification. We consider cost and non-ideal behavior. We adjust or stop the test if errors, verification or conditions of use make the solution unsuitable for the agreed activity.
From you Assessment of persons knowledgeable of pregnancy and approval of continuance.
We integrate with limits and liability.
If the test justifies the use, we establish the route of the result, the verification and the manual alternative. We define who monitors quality and how data or behavior changes are handled. Extension to other duties is decided upon re-evaluation, not by assumption.
From you Responsible for use and rules for inspection, access and maintenance.
A WORKING EXAMPLE
Technical requests.
A summary for verification.
A company receives service requests with texts, photos and documents of different forms. The responsible colleague must identify the equipment, the problem described and the information still needed.
- Preparation uses permitted sources.
- The solution can organize the information present in the request and indicate where it comes from. It does not make assumptions about the series, condition of the equipment or the cause of the defect and does not produce a diagnosis on its own.
- Deficiencies remain visible.
- An illegible series or two conflicting descriptions are flagged. The colleague checks the source and asks for clarification. A proposed response may be prepared but is reviewed before communication to the client.
- Utility is assessed in work.
- We compare the preparation and verification time with the previous mode. If the syntheses omit important details or require frequent corrections, we change the assignment delimitation or keep the manual alternative. The technical acceptance of the case remains with the authorized person.
RELATED CAPABILITIES
What the use of AI does
controllable.
The permitted data, the route of the result and its verification are as important as the mechanism that prepares it.
Process mapping
Identifies the activity and point where assisted training may be useful.
Workflow automation
May include verified output in a route with human rules and interventions.
Documents & approvals
Retains verification and authorization of documents prior to use.
Content & copywriting
Provides editing and proofreading for materials that must accurately represent the firm.
Questions about
use of AI.
How we choose a suitable task and how we check whether the result can be used.
How do we know if we need AI?
We start from activity, volume and difficulty. If the problem can be solved by stable rules or by removing a step, that option may be more appropriate. AI is worth testing when it brings verifiable help to variable information at acceptable cost and risk.
Can you guarantee correct answers?
Not for every case. We define the evaluation criteria and test representative situations, including difficult ones. Results may contain omissions or errors. We choose verification and usage limits based on their effect, without presenting a successful demonstration as proof of permanent correctness.
Can company data be sent to any AI service?
No. We check sources, permissions and processing conditions before the test. The company authorizes the use of appropriate data for the task. We may limit or remove information that is not necessary. The choice of implementation also depends on these conditions, not just on the quality of a response.
Can AI make business or technical decisions for us?
For this collaboration, we explicitly delimit what a person can propose and what must be approved. We do not automatically assign the authority to accept terms, diagnose or hire the firm. Tasks with important consequences require appropriate checks and rules, validated with those in charge.
Must we build our own model?
We do not assume this. We evaluate implementation options for the agreed task and conditions of use. Sometimes an existing mechanism is sufficient, other times the solution is not worth continuing. Developing a software product or training your own model is not automatically included.
How do we measure if the solution helps?
We compare correctness, time saved and verification effort on the same types of cases. We also track important errors, cost and cases that need to be done manually. A quick result is not a win if its verification takes more time or introduces unacceptable risks.
What happens when the result is unclear?
We determine how the limit is signaled and where the case reaches for verification. The solution does not have to continue just to produce a complete answer. We keep the sources and the right manual alternative so that the colleague can decide without relying on made up information.
Can we later extend the solution to other activities?
Yes, after evaluating the new task, the data and the consequences of the errors. Good results in one context do not guarantee behavior in another. Expansion has its own criteria and checks, and maintenance and quality tracking are collaboratively delineated.
What a task it deserves
in practice tested?
Show us some examples of work and what result would help the colleague checking them. We can assess whether the AI is suitable and what limits should be kept.