01 / GATHER
Data gathering
Collect and structure information from approved sources, with references your team can follow.
- Literature and evidence monitoring
- Document and metadata extraction
- Database and API updates
Syntara by Tenseq
Automate the work
between data
and decisions.
Syntara brings AI agents into your team to gather data, run workflows and deliver analysis. Give your team more time for the science, with fewer manual handoffs and a shorter turnaround time.
Built for biotech, CROs and CDMOs.
SYNTARA WORKFLOWILLUSTRATIVE
From scattered inputs
to a reviewed result.
-
01
SOURCES
Gather
Documents, databases and approved APIs
-
02
WORKFLOW
Orchestrate
Validate inputs and route the next action
-
03
RESULTS
Analyse
Run methods and prepare the evidence
-
04
CONTROL
Review and deliver
Human approval where your process needs it
Source traceability • Defined checkpoints
• Your systems
Less data gathering by handFewer repetitive handoffsMore time for interpretation
What you can automate
AI agents built around
your team's work.
Start with one recurring task. Connect the tools you already use, define the checks that matter, and build a workflow you can measure.
02 / AUTOMATE
Workflow automation
Connect routine steps across your systems and move work forward with explicit approval points.
- Data intake and validation
- Analysis pipeline orchestration
- Reporting and exception routing
03 / ANALYSE
Data analysis
Turn recurring analyses into reproducible workflows, with outputs ready for expert review.
- Quality control and anomaly flags
- Comparisons and trend reports
- Evidence summaries and dashboards
Every use case is scoped to your data, permissions and validation requirements.
See what Tenseq can build
Your scientific question.
A team of agents.
Explore a complete RNA-seq project. Inspect the evidence, approve a pipeline, resolve a sample-quality exception and question the analysis, all through a conversation.
PROJECT / RNA-SEQ PILOT
Does treatment change gene expression?
12 synthetic samples · 2 conditions · 2 balanced batches
Study coordinator01 / 08
Define the study before writing code
See how the agents translate your question into a plan you can inspect.
Ready to explore
Real method references, synthetic inputs and outputs. Playback simulates agent activity; it does not search, run pipelines or analyse your data.
Explore Vector QC ↗Embedded in your team
Your tools. Your datasets.
New questions to explore.
We start with one workflow and the data you already use. Agents connect the recurring work, while your scientists set the priorities and your team retains ownership of the methods and decisions.
Fit the existing workflow
Map your ELN, LIMS, storage and analysis environment. Agree on the interfaces, permissions, triggers and where reviewed outputs should return.
Give each person a clear role
Scientists ask questions in plain language. Data teams own the pipelines. Named reviewers approve exceptions and interpretation in the process your team already follows.
Build on what you already know
Assess historical studies, assay results and process records for new comparisons. Turn findings into hypotheses, follow-up experiments and product improvement plans your team can validate.
Begin with a scoped pilot, then expand after the team reviews data quality, scientific usefulness and operational impact.
Have a workflow in mind? Let's explore what agents could do for your team.
Discuss your workflow ↗Operational impact
Make turnaround time
a measurable outcome.
Turnaround time (TAT) is the elapsed time from receiving an input to delivering a reviewed result. We identify the bottlenecks before choosing what to automate.
Reduce active work
Replace repeated collection, formatting and transfer steps so specialists can focus on interpretation.
Reduce waiting
Trigger the next step when inputs are ready and surface exceptions before they hold up delivery.
Measure the change
Compare elapsed TAT, hands-on time, rework and output quality against your existing process.
Pilot success criteria
Baseline → Automate one workflow → Validate quality → Compare results
Targets are agreed during scoping. Actual improvements depend on the workflow and are measured in the pilot.
From first workflow to production
Start with a focused pilot.
A bounded first project gives you evidence to decide what to scale next.
01
Scope
Map the task, data access, baseline TAT and acceptance criteria.
02
Build
Connect your tools and implement the agent with checks and review points.
03
Validate
Test representative inputs, failure cases and outputs with your experts.
04
Deploy and hand over
Deliver documented software, operating guidance and an agreed support plan.
Scientific engineering, applied
Automation with
scientific rigour.
Tenseq is a software and data partner for life science research and operations. Our work spans bioinformatics pipelines, machine learning, scientific software and laboratory data systems.
AI agents are built around defined methods, traceable sources and human review. We scope access controls, logs and validation requirements to your environment.
You own the software we deliver. Your team can run, inspect and extend it.
Tell us where work gets stuck
Which task would
you automate first?
Share the recurring task, the systems involved and the result you need. We can discuss a focused pilot and how to measure its impact.
info@tenseq.com ↗For pipeline projects: bioinformatics@tenseq.com