DATAContact

KDATA CAPABILITIES

Built to search, screen, and benchmark at scale.

Move from a broad company universe to a reasoned comparable set through structured search, AI-assisted review and evidence that remains connected to each decision.

FIND THE RIGHT STARTING UNIVERSE

Search & Discovery

Combine structured criteria with meaning-based discovery, then move quickly from a broad market to relevant candidates.

Multi-filter Search

Build a screening path from layered geography, classification and financial criteria. Each condition narrows the universe while leaving the rationale visible for review.

  • Combine country and regional criteria in one search.
  • Layer sector, industry and NACE classifications.
  • Screen revenue, net income, assets and equity ranges.
  • Review available history and the 3-year consecutive loss flag.

In practice: Create a broad longlist for a comparable search, then tighten it through criteria that can be explained in the working paper.

Semantic Smart Search

Find companies by the meaning of their activities, not exact wording alone. Semantic profiles connect business descriptions, revenue segments and classification evidence, with keyword search available as a fallback.

  • Describe the activity or business model in natural language.
  • Draw on revenue segments covering up to 5 years.
  • Use primary NACE evidence to strengthen context.
  • Fall back to keyword discovery when a direct term is more useful.

In practice: Widen discovery to companies whose activities are relevant even when their public descriptions use different terminology.

Company Lookup

Move directly to a known company by searching its name or ticker. Clear identity cues help distinguish similarly named entities before deeper review.

  • Search by company name or exchange ticker.
  • Recognise entities through available logo and listing details.
  • Open the company profile without rebuilding a broader search.

In practice: Revisit a known comparable or verify a named company quickly during a review discussion.

TURN QUESTIONS INTO A REVIEW PATH

AI-assisted querying

Use AI to structure an initial query and challenge individual candidates while keeping the analyst in control.

Query Builder with AI Assist

Describe the target company profile in natural language and turn that request into a structured starting query. The proposed filters remain visible for the analyst to review and adjust.

  • Translate a research question into structured criteria.
  • Surface the proposed filters before running the search.
  • Refine or remove criteria without losing the original intent.

In practice: Start a sector or regional screening exercise from the engagement brief, then validate every filter before relying on the result.

Per-company AI Evaluation

Review how an individual company aligns with the stated comparable rationale. AI assists the screening — the final call is always the analyst's.

  • Read the company profile against the research question.
  • Highlight evidence that supports or weakens comparability.
  • Keep the assessment connected to the underlying company record.

In practice: Challenge a borderline candidate before deciding whether it belongs in the shortlist or requires a documented exclusion.

KEEP THE EVIDENCE TOGETHER

Review & Organise

Inspect each company in depth and organise candidates around the engagement they support.

Company Detail

Bring business context, source metadata and multi-year financial evidence into one company profile. Original-currency and USD-normalised values sit alongside business and regional segment detail.

  • Review the business description and source metadata.
  • Compare financial history across available years.
  • Inspect original-currency and USD-normalised values.
  • Examine business and regional segment evidence.

In practice: Test whether a candidate's activities and financial profile support inclusion before finalising a comparable set.

Baskets

Group selected companies into a working set tied to the research task. Move from longlist to shortlist without losing the candidates already reviewed.

  • Add candidates directly from search results.
  • Keep separate working sets for different analyses.
  • Reopen a basket to continue review and refinement.

In practice: Maintain a defensible working set while analysts discuss inclusions, exclusions and follow-up checks.

MOVE FROM SHORTLIST TO WORKING PAPER

Benchmark & Export

Carry the selected evidence into analysis and reporting without breaking the link to the screening rationale.

Data Export

Export selected company and financial evidence for analysis outside the screening interface. The output supports working papers without disconnecting the data from the reviewed company set.

  • Choose the company and financial fields needed for review.
  • Export a selected comparable set in a workable format.
  • Carry structured evidence into supporting schedules.

In practice: Prepare the evidence base for a benchmarking working paper after the shortlist has been reviewed.

TP Market Range

Run a consistent percentile analysis across a selected comparable set. The result remains tied to the companies and screening decisions that support it.

  • Apply the chosen financial indicator to the comparable set.
  • Review quartiles, median and the resulting market range.
  • Keep the calculation connected to its underlying observations.

In practice: Produce a market-range view for Transfer Pricing analysis after the comparable set and indicator have been agreed.

ONE RESEARCH FOUNDATION

Every capability runs on KDATA's own data and infrastructure.

The result is a connected workflow: discovery, screening, review and benchmarking all draw from the same structured evidence.

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