Reconcile biological inputs
Reconcile biological inputs using the current biological dataset and its supporting records.
BIOLOGICAL DATASET / ANALYSIS PIPELINE / REFERENCE VERSION
AI agent for computational biology teams
Make the analysis reproducible from sample to result.
Reconcile biological inputs, run reproducible analyses and deliver analysis artefacts—carried through by an autonomous AI agent that adapts to your work.
A specialised AI agent you can ask for more. Chat here on the site.
$249.00/month, including $50.00 in monthly credits. Monthly subscription. Before tax.
Specialised AI agentYour own report presetNo connection needed to begin
Biological dataset workbenchSPECIALIST / 1140
YOUR SPECIALISED AI AGENT
One place for reconcile biological inputs, run reproducible analyses, deliver analysis artefacts.
MADE FOR YOUR WORKDAYStart with work already on your desk
Your first useful assignment.
Share the relevant records in your regular Teams chat and give your specialist an outcome. No model or tool choices to make first.
Get my AI agent →YOU COULD ASK
“Reproduce the expression analysis, resolve sample mapping errors and deliver the versioned results with a clear limitations report.”
- Reconcile biological inputs
- Run reproducible analyses
- Deliver analysis artefacts
A useful first result
An editable biological dataset and reference version register with traceable references, resolved discrepancies and specific next actions.
Example assignment, not a promised delivery time. Your agent works from the records and access you provide. Add connections when you want it to act in other systems.
A conversation at your desk
Reproduce the expression analysis, resolve sample mapping errors and deliver the versioned results with a clear limitations report.
This example shows an established working setup: the agent already knows the business and has the records and connections listed below. Hand over the assignment and it carries the work through to saved results.
Illustrative project · configured agent at work
A configured agent with your biological dataset, analysis pipeline, reference version records, current commitments and the connected accounts used to run reproducible analyses.
- Reconcile biological inputs
- Run reproducible analyses
- Deliver analysis artefacts
A working specialist can handle the project, with a configured team sharing the work where available. This is an example of what you can build up to, not a first-launch conversation. Timing and usage depend on the assignment.
- You · Computational biology teams
Reproduce the expression analysis, resolve sample mapping errors and deliver the versioned results with a clear limitations report.
- Your AI agent · AI Agent Store
Two sample names map to different metadata rows and the reference version changed. I am reconciling both before rerunning the comparison.
Work in progress - You · Computational biology teams
Keep the original filtering criteria and report batch effects explicitly.
- Your AI agent · AI Agent Store
The analysis pack includes reproducible runs, corrected sample mapping and results linked to reference versions and stated limitations. Your working files and dashboard reflect the recorded outcomes.
Later · delivery and saved results
Built around the details that matter
Reconcile biological inputs. With the right questions already in mind.
Computational biological analysis, distinct from wet-laboratory service operations and general data science consulting.
Run reproducible analyses
Run reproducible analyses while keeping analysis pipeline changes traceable.
Deliver analysis artefacts
Deliver analysis artefacts with checked reference version evidence and editable working files.
BIOLOGICAL DATASET / ANALYSIS PIPELINE / REFERENCE VERSION
A working brief for your AI agent.
Track sample identifiers, reference assemblies, pipeline parameters and computational environments. Preserve missingness and batch effects and distinguish computational findings from biological validation. Your specialist maintains these working records in regular Teams as the assignment develops.
AI
Your AI agentPrepared for computational biology teams
Biological dataset and reference version registerEXAMPLE, NOT LIVE ACTIVITY
An example record for biological dataset, analysis pipeline, reference version, analysis checks.
01Biological dataset02Analysis pipeline03Reference version
The useful next step
Reconcile biological inputs
Reconcile biological inputs using the current biological dataset and its supporting records.
The details your specialist follows
Working records for computational biology teams
Follow each biological dataset through the working record. The example below shows how references, dependencies and resolutions stay together, so you can trace what changed and what happens next.
Keep an eye on: changes in analysis pipeline, missing reference version evidence and unresolved biological dataset dependencies.
Illustrative working document · your records start empty
| Biological dataset | Dependency to follow | Evidence of completion |
|---|---|---|
| OPPORTUNITY114-104 | Reconcile biological inputs | Sample mapping errors resolved |
| OPPORTUNITY114-118 | Run reproducible analyses | Analysis runs reproduced |
| OPPORTUNITY114-126 | Deliver analysis artefacts | Biology analysis milestones met |
Active biological dataset records3
Analysis checks across the planned work400 analysis checks
Outcomes with supporting evidence2
Illustrative project figures. Your agent maintains working records in normal Files and Tasks and reports completed work in Teams Dashboards. Ask it to adapt those records and reports as your workflow changes.
A small tool for a real job
Analysis checks across the planned work. Before the back-and-forth.
Watch for changes in analysis pipeline, missing reference version evidence and unresolved biological dataset dependencies. Investigate the cause, carry useful follow-through to completion and improve the saved method when it helps.
Try the numbers, then bring the confirmed brief to your specialist. Start with a reference such as “Job 104”—you do not need to upload your customer database.
- Biological dataset
- Analysis pipeline
- Reference version
- Analysis checks
Try your own numbers
Analysis checks across the planned work
Planning arithmetic from your inputs. Your specialist can turn the confirmed details into a working brief. This is not a quote or capacity commitment.
From enquiry to a useful next step
Biological dataset and reference version register. Your way of working.
01
Brief and source records received
Add a job reference and the details you already know.
02
Reconcile biological inputs
Biological dataset and Analysis pipeline, ready to check.
03
Run reproducible analyses
Give your specialist the outcome and let it carry the work forward.
04
Outcome evidence verified
Keep working records in Files and Tasks.
05
Deliver analysis artefacts
Keep the job and its next steps together.
Ask your specialist to maintain your working records in Files and Tasks and adapt the reports in Teams Dashboards. These are the regular platform tools, prepared for your business. Connections to other systems are optional.
Prepared for your business
The numbers behind your ai agent for computational biology teams.
Six useful measures, ready in your regular Teams dashboard. Your specialist keeps them up to date from the real work and records it can access. Ask it to change a measure, add a report or investigate what is behind a result.
Recorded cases · outcomes
Biology analysis packs delivered
What this measures
Count one distinct observed outcome for “Biology analysis packs delivered”, linked to its biological dataset and stable completion-event ID. Use the real completion timestamp and supporting reference version reference. Pending, merely promised, repeated and illustrative outcomes do not count. Revisions or corrections replace the original event rather than creating duplicate completions.
Recorded cases · outcomes
Sample mapping errors resolved
What this measures
Count one distinct observed outcome for “Sample mapping errors resolved”, linked to its biological dataset and stable completion-event ID. Use the real completion timestamp and supporting reference version reference. Pending, merely promised, repeated and illustrative outcomes do not count. Revisions or corrections replace the original event rather than creating duplicate completions.
Recorded cases · outcomes
Analysis runs reproduced
What this measures
Count one distinct observed outcome for “Analysis runs reproduced”, linked to its biological dataset and stable completion-event ID. Use the real completion timestamp and supporting reference version reference. Pending, merely promised, repeated and illustrative outcomes do not count. Revisions or corrections replace the original event rather than creating duplicate completions.
Average · hours
Validated inputs received to analysis delivery
What this measures
Measure “Validated inputs received to analysis delivery” in elapsed hours using the actual start and end timestamps for the same biological dataset. Store one duration per completed observation with source references and timezone. Exclude pending cases, missing timestamps, reversed intervals and illustrative values. Never substitute a current status or sum recurring snapshots.
Observed rate · %
Biology analysis milestones met
What this measures
Completed commitments assessed for “Biology analysis milestones met”, linked to stable biological dataset references. Numerator: on_time; denominator: on_time plus late from the same assessed population. Classify each observation once and preserve failures. Exclude pending or unknown cases, unassessed records and illustrative values. Compare actual completion with the original agreed deadline and timezone; never move the deadline to improve the result. Corrections replace the original event instead of counting twice.
Observed rate · %
Pipeline reproducibility checks passed
What this measures
Completed assessments for “Pipeline reproducibility checks passed”, linked to stable biological dataset references. Numerator: passed; denominator: passed plus failed from the same assessed population. Classify each observation once and preserve failures. Exclude pending or unknown cases, unassessed records and illustrative values. Apply the documented requirement or acceptance checklist and retain the actual supporting evidence; do not infer a pass from an administrative status. Corrections replace the original event instead of counting twice.
Your reports start empty. Figures reflect observed work in the selected period; missing records are not treated as measured zeroes. Current jobs, schedules and stock stay in your working files and tasks. You and your agent can adapt the dashboard as your business changes.
Keep the tools that already work
Specialised in your trade. Open to your next idea.
Your AI agent starts with the details of your business already in mind. The three workflows are starting points. Ask it for help with another task in the same conversation.
An AI agent prepared for your business
The starting instructions, intake questions and working rules already match computational biology teams. Add your service list and one example of your tone when you are ready.
Give your agent the whole job
Give your agent an outcome, the access it needs and your budget. It handles routine decisions and follows through in your connected tools, asking when a missing business decision needs you. Set or change your own boundaries whenever you want.
Ask beyond the starting workflows
Your agent can take on a new assignment, build a new workflow, improve its saved methods and adapt its dashboard as your needs evolve. The starting examples do not limit its capabilities. Give your agent another task in plain language. What it can carry out depends on the tools you connect and the access you choose.
Recommended starting plan
Your AI agent for computational biology teams.
Begin with one specialist and a report preset for your business. After setup, your regular Agent Teams conversation opens. No tool selection before your first conversation.
$249.00/month
Capacity for 1 running agent · hosting throughout the month
Start my AI agent →A clear starting total
Your Agent plan is $199/month with hosting included. This recommended offer adds $50 in monthly Platform Credit funding, for $249/month.
$249.00 before tax today and monthly, including $50.00 in monthly Platform Credit funding.
Credits offer an easy start and unused credits roll over. You can switch to your own subscriptions or API keys anytime, with no Platform Credits needed for that AI work. Hosting is included within your agent’s resource allocation. Optional voice and other services use credits.
Hosting availability is checked before payment. Create your account, confirm your monthly subscription, then start chatting when your specialist is ready. Your instructions and dashboard are prepared automatically after payment.
Other plans and connection options →
Prefer your own AI accounts? Choose a plan without the extra $50 monthly credit funding using the options above. Changing AI access alone does not remove that funding. Automatic top-ups start off; buy credits or set top-ups and spending limits when needed. Keep the full Teams controls for models, connections and supported settings.
Before you begin
An AI agent for computational biology teams.
How does the agent keep biological dataset work accurate?
Track sample identifiers, reference assemblies, pipeline parameters and computational environments. Preserve missingness and batch effects and distinguish computational findings from biological validation. It keeps source references with the working files, checks the result and follows up on discrepancies. Ask it to adjust the method or report whenever your needs change.
Is this only for the three tasks shown?
No. This is an AI agent specialised for computational biology teams, with three prepared workflows to get you started. You can ask for writing, research, planning, analysis or another task in the same chat. It can make routine decisions and carry the work through using available tools. You set its boundaries; it asks when a real business decision or missing access needs you.
What happens after I subscribe?
After payment, we prepare your AI agent for computational biology teams, its workflows and your biological dataset and reference version register dashboard. Your regular Agent Teams chat opens when setup is ready. Files, Tasks, settings and billing work the same as for other customers. Reports start empty and count actual reported work; the examples above are illustrative.
Do I need to connect email or messaging?
No. Prepare your first brief and draft here. Connect external channels later when you want the specialist to work there.
Does this replace my existing business software?
Use it alongside your current tools for preparing work and organising jobs. Live stock, availability, sending and payment records require a verified connection or your own confirmation.
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