Private Company First-Pass Toolkit
Collection
Open evidence-first worksheets and browser tools for private-company screening and diligence. Maintained by DDScore.ai. • 8 items • Updated
Dataset Preview
The full dataset viewer is not available (click to read why). Only showing a preview of the rows.
The dataset generation failed
Error code: DatasetGenerationError
Exception: ArrowInvalid
Message: Failed to parse string: 'Programme lead' as a scalar of type double
Traceback: Traceback (most recent call last):
File "/usr/local/lib/python3.14/site-packages/datasets/builder.py", line 1837, in _prepare_split_single
writer.write_table(table)
~~~~~~~~~~~~~~~~~~^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/arrow_writer.py", line 765, in write_table
self._write_table(pa_table, writer_batch_size=writer_batch_size)
~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/arrow_writer.py", line 773, in _write_table
pa_table = table_cast(pa_table, self._schema)
File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2369, in table_cast
return cast_table_to_schema(table, schema)
File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2303, in cast_table_to_schema
cast_array_to_feature(
~~~~~~~~~~~~~~~~~~~~~^
table[name] if name in table_column_names else pa.array([None] * len(table), type=schema.field(name).type),
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
feature,
^^^^^^^^
)
^
File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 1852, in wrapper
return pa.chunked_array([func(chunk, *args, **kwargs) for chunk in array.chunks])
~~~~^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2143, in cast_array_to_feature
return array_cast(
array,
...<2 lines>...
allow_decimal_to_str=allow_decimal_to_str,
)
File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 1854, in wrapper
return func(array, *args, **kwargs)
File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2006, in array_cast
return array.cast(pa_type)
~~~~~~~~~~^^^^^^^^^
File "pyarrow/array.pxi", line 1147, in pyarrow.lib.Array.cast
File "/usr/local/lib/python3.14/site-packages/pyarrow/compute.py", line 412, in cast
return call_function("cast", [arr], options, memory_pool)
File "pyarrow/_compute.pyx", line 604, in pyarrow._compute.call_function
return func.call(args, options=options, memory_pool=memory_pool,
File "pyarrow/_compute.pyx", line 399, in pyarrow._compute.Function.call
result = GetResultValue(
File "pyarrow/error.pxi", line 155, in pyarrow.lib.pyarrow_internal_check_status
return check_status(status)
File "pyarrow/error.pxi", line 92, in pyarrow.lib.check_status
raise convert_status(status)
pyarrow.lib.ArrowInvalid: Failed to parse string: 'Programme lead' as a scalar of type double
The above exception was the direct cause of the following exception:
Traceback (most recent call last):
File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 1369, in compute_config_parquet_and_info_response
parquet_operations, partial, estimated_dataset_info = stream_convert_to_parquet(
~~~~~~~~~~~~~~~~~~~~~~~~~^
builder, max_dataset_size_bytes=max_dataset_size_bytes
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
)
^
File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 948, in stream_convert_to_parquet
builder._prepare_split(split_generator=splits_generators[split], file_format="parquet")
~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/builder.py", line 1683, in _prepare_split
for job_id, done, content in self._prepare_split_single(
~~~~~~~~~~~~~~~~~~~~~~~~~~^
gen_kwargs=gen_kwargs, job_id=job_id, **_prepare_split_args
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
):
^
File "/usr/local/lib/python3.14/site-packages/datasets/builder.py", line 1869, in _prepare_split_single
raise DatasetGenerationError("An error occurred while generating the dataset") from e
datasets.exceptions.DatasetGenerationError: An error occurred while generating the datasetNeed help to make the dataset viewer work? Make sure to review how to configure the dataset viewer, and open a discussion for direct support.
checkpoint string | dimension string | review_question string | mentor_owner null | evidence_to_request null | observed_change null | next_action null |
|---|---|---|---|---|---|---|
Intake baseline | Business Idea | What problem and initial assumption should the programme track? | null | null | null | null |
Intake baseline | Offering | What exactly is offered and what has been tested with users? | null | null | null | null |
Intake baseline | Team | Which roles and capability gaps are visible at intake? | null | null | null | null |
Intake baseline | Market | Which customer segment and demand evidence support the starting story? | null | null | null | null |
Intake baseline | Competitors | Which alternatives do customers use today? | null | null | null | null |
Intake baseline | Technology & IP | What technical dependency or ownership question needs attention? | null | null | null | null |
Intake baseline | Scalability | Which part of delivery still depends on founder effort? | null | null | null | null |
Intake baseline | Legal & Regulatory | Which permission obligation or compliance assumption is material? | null | null | null | null |
Intake baseline | Exit | What plausible strategic outcome frames the long-term story? | null | null | null | null |
Intake baseline | Presentation | Which important claim is least supported in the current deck? | null | null | null | null |
Intake baseline | Financial Critique | Which runway cost or unit-economics assumption is most fragile? | null | null | null | null |
Intake baseline | Fundability | Which evidence gap most limits an investor introduction today? | null | null | null | null |
Mid-programme | Business Idea | What did customer or market learning change about the core problem? | null | null | null | null |
Mid-programme | Offering | What has moved from concept to tested product evidence? | null | null | null | null |
Mid-programme | Team | Which role gap closed and which one still blocks progress? | null | null | null | null |
Mid-programme | Market | What new demand evidence supports or contradicts the chosen segment? | null | null | null | null |
Mid-programme | Competitors | What changed after testing the product against real alternatives? | null | null | null | null |
Mid-programme | Technology & IP | Which technical risk or IP question has been resolved? | null | null | null | null |
Mid-programme | Scalability | What repeatable process now replaces manual founder work? | null | null | null | null |
Mid-programme | Legal & Regulatory | What compliance work is complete and what remains unowned? | null | null | null | null |
Mid-programme | Exit | Does new market evidence change the plausible long-term outcome? | null | null | null | null |
Mid-programme | Presentation | Which deck claim now needs updating to match the business? | null | null | null | null |
Mid-programme | Financial Critique | How did runway costs or unit economics move against plan? | null | null | null | null |
Mid-programme | Fundability | What must still become true before investor introductions? | null | null | null | null |
Pre-demo-day | Business Idea | Can the team state the problem with current evidence rather than aspiration? | null | null | null | null |
Pre-demo-day | Offering | Does the demo prove the value claimed in the pitch? | null | null | null | null |
Pre-demo-day | Team | Can the team explain ownership gaps and a credible hiring plan? | null | null | null | null |
Pre-demo-day | Market | Do current customer signals support the market narrative? | null | null | null | null |
Pre-demo-day | Competitors | Does the pitch acknowledge credible alternatives and differentiation? | null | null | null | null |
Pre-demo-day | Technology & IP | Are material technical and ownership risks disclosed clearly? | null | null | null | null |
Pre-demo-day | Scalability | Is the growth model supported by a repeatable operating mechanism? | null | null | null | null |
Pre-demo-day | Legal & Regulatory | Are material regulatory dependencies visible to investors? | null | null | null | null |
Pre-demo-day | Exit | Is the long-term outcome framed as a scenario rather than a promise? | null | null | null | null |
Pre-demo-day | Presentation | Do claims in the deck match the latest operating evidence? | null | null | null | null |
Pre-demo-day | Financial Critique | Are runway use of funds and key assumptions internally consistent? | null | null | null | null |
Pre-demo-day | Fundability | Which unresolved gap should be fixed before an investor introduction? | null | null | null | null |
Intake baseline | Team | Which roles and capability gaps are visible at intake? | null | null | null | null |
Intake baseline | Market | Which customer segment and demand evidence support the starting story? | null | null | null | null |
Mid-programme | Technology & IP | Which technical risk or IP question has been resolved? | null | null | null | null |
Mid-programme | Financial Critique | How did runway costs or unit economics move against plan? | null | null | null | null |
Pre-demo-day | Presentation | Do claims in the deck match the latest operating evidence? | null | null | null | null |
Pre-demo-day | Fundability | Which unresolved gap should be fixed before an investor introduction? | null | null | null | null |
Accelerator Cohort Review Template
A reusable programme-operations template for accelerator and incubator teams that want a consistent review rhythm across a mixed startup cohort.
The main CSV contains 36 blank working rows: the same 12 private-company dimensions at three checkpoints — intake baseline, mid-programme and pre-demo-day. Each row gives a neutral review question plus fields for a mentor owner, evidence request, observed change and next action. The second CSV is a six-row completely fictional example that shows how a programme team might assign attention without ranking companies.
Files
accelerator-cohort-review-template.csv— 12 dimensions × 3 checkpoints.fictional-focus-example.csv— six illustrative focus items for a fictional programme.
Suggested workflow
- At intake, record the starting evidence state and assign the mentor who will own the largest open question.
- At midpoint, record what changed and which assumption still needs testing.
- Before demo day, recheck whether the current materials support the claims founders will make to investors.
- Compare observations rather than forcing them into one cohort ranking.
The structure is based on the 12 dimensions used in DDScore's private-company review framework. DDScore analyses submitted materials, checks claims against current public information and surfaces strengths, risks, missing evidence and next questions. It supports rather than replaces programme and mentor judgement.
Guardrails
- This template does not score, rank, accept or reject a company.
- It does not verify an answer or replace full due diligence.
- Use synthetic or non-confidential information in shared workshops.
- Adapt domain-specific evidence requests for health, climate, fintech and other regulated programmes.
License
CC BY 4.0. Attribution: Playful Pixels Oy / DDScore.ai.
- Downloads last month
- 24