Complete Platform Capability Map.

Upload the brief in any format — PDF, Word, email paste, or typed text. PitchWhich reads it automatically and extracts structured signals: budget present or absent, deadline, seniority cues in language, number of agencies mentioned, and specificity of stated objectives. No manual data entry required.

PitchWhich evokes factors like decision-maker seniority (CMO vs. procurement), budget transparency, and measurable KPIs. We identify if a brief is a genuine growth engine or a courtesy filler, ensuring you only resource pitches that are actually winnable regardless of your fit.

Evaluates how well you match the client *today*. Scores focus on sector lineage (wins in the category), prior trust with decision-makers, and economic ROI (fee vs. pitch cost). We eliminate "ego-pitching" by highlighting when a team is chasing work that doesn't align with their proven track record.

The single biggest reason challenger pitches lose. We use deep database statistical insight to scan the established agency's tenure, recent wins, and relationship lock-in. We price in the "mountain you're climbing" by identifying relief signals like stale work or public criticism that create vulnerability.

Pitching isn't free—it costs billable hours. We mix real-time utilization data with AI power to evaluate agency "hunger." Busy shops (90%+ utilized) lose points to protect operations; hungry shops needing funnel velocity gain a strategic tailwind.

Produces a single index from all 4 pillars, mapped to 5 zones: Strong Win (86+), Likely Win (71–85), Deliberate (55–70), Lean No-Go (36–54) and No-Go (0–35). Partial data produces an honest confidence interval (e.g., "Score: 64 ± 8"), flagging exactly where more research is needed to eliminate uncertainty.

For every pillar, PitchWhich shows exactly where to find supporting evidence and provides direct links: "Open LinkedIn — search the brief sender — CMO is +12 pts." Every research task is estimated and guided. A full analysis takes 2–4 hours, not days, fueled by deep database insights.

When the score falls in the "Deliberate" or "Lean No-Go" zones, a conversational AI walks the agency through a structured decision dialogue. It challenges assumptions, surfaces overlooked factors, and produces a written recommendation with full rationale that mixes database truth with AI reasoning.

Guides the research to check LinkedIn for recent CMO or VP Marketing appointments at the client. A new CMO hired in the last 6 months is the strongest single predictor that a pitch is genuine — new executives almost always want their own agency. A new procurement head signals a cost-reduction pitch where the incumbent is statistically likely to be retained (research: AAR Group, R3 Worldwide).

Guides research to check the Meta Ad Library and Google Ads Transparency Center for the client brand. Examines number of active ads, first and last shown dates, campaign continuity, and EU reach signals. Active campaigns confirm the client is investing. Inactive for 6+ months signals a budget under pressure, an in-housing strategy, or a pitch that may be cancelled after award.

Links directly to the relevant public financial registry — SIREN for France, Companies House for the UK, InfoCamere for Italy, CRO for Ireland — with precise instructions to verify the last three years of revenue, EBITDA trend, and headcount changes. Flags if the declared budget is implausible relative to the company's reported revenues and financial trajectory.

When the optional database add-on is active, PitchWhich pulls exact cross-media investment data by advertiser: TV, Radio, Print, OOH, and Digital — actual spend in euros, year-on-year trend, and channel mix breakdown. Sources: Nielsen Italy Media Compass, Infoadex (Spain), Nielsen IBOPE (Mexico). Shifts the economic value factor from a ±200% rough estimate to ±10% precision, recalibrating the model weighting from 60/40 to 70/30 external/internal.

Upload past work once: sector, client type, challenge, channels used, and measurable outcome. The system indexes entries using semantic search, not keyword matching. A campaign described as "brand awareness for FMCG launch" will surface correctly when a brief arrives for "consumer goods visibility activation" — because intent is understood, not just words.

When a brief is entered, the system automatically identifies the 3 most relevant case studies from the library. Matching considers sector alignment, challenge type, channel overlap, and client size similarity. A relevance score is shown for each result so the team understands why each was selected and can choose between close matches.

Each case study can be tagged with measurable outcomes: reach, conversion lift, brand awareness uplift, award won, media coverage, or client retention. Tags are searchable and filterable. The system can surface "only case studies with a measurable ROI proof point" when a brief specifically mentions performance accountability or data-driven evaluation criteria.

Compares the agency's case study library against the sectors of briefs received over the past 12 months. Identifies where the agency is receiving pitches in sectors where it has weak or zero credentials. Flags clearly: "You have received 4 briefs in Financial Services but have 0 case studies in that sector." Updates automatically as new briefs arrive.

Case studies can be updated as results evolve — a campaign still in market can have updated metrics added without losing the original entry. Version history shows how a case study has developed. Archived cases remain searchable for historical context even after the agency no longer actively pitches them.

Every brief entered creates a pitch record with defined status stages: Brief Received → Scored → Decision Made → Active → Submitted → Outcome. Tracks deadline, decision date, submission date, and result (won, lost, or withdrawn). A visual pipeline view shows all active pitches and their stages simultaneously.

Team members log hours spent per pitch — research, strategy, creative, deck production, meetings. Aggregated in real time. If a pitch is abandoned or lost, the hours are preserved as historical cost data. Integration available with Harvest, Toggl, and Notion for agencies already using those tools for time tracking.

Calculates the true cost of new business acquisition: total hours across all pitches × blended rate ÷ pitches won = actual cost to acquire one client. Produces an annual view: "You spent €142,000 on pitches last year. You won 2. Each client cost €71,000 to acquire." This is the number that changes agency behaviour permanently.

After 12 months of data, produces a full win rate breakdown: which sectors the agency wins most frequently, which client sizes, which brief types (rebrand, campaign, annual AOR, project), and which pitch processes. Year-over-year comparison activates once Year 2 data is available. Data feeds directly back into the personalised scoring model.

When a pitch outcome is recorded, the system prompts for a short structured debrief: what was the deciding factor, what would the team do differently, was the go/no-go score correct in retrospect? Lost pitches have additional fields: who won, why (if known), and was the pitch fee reasonable. The debrief takes under 10 minutes and captures learning at the moment it's freshest.

An annual summary of value generated: pitches declined with their estimated avoided cost, pitches participated in, win rate vs. the 22% industry benchmark, total hours saved in brief qualification, and additional pitches made possible by freed bandwidth. Exportable as a one-page PDF. Updates automatically as the year progresses.

If competing agencies are named in the brief, PitchWhich analyses their public websites (Work, Clients, About sections), LinkedIn company pages, and recent award credits. Extracts: declared sectors, visible clients, approximate team size, recent case study themes, and growth or decline signals from employee count trends over the past 12 months.

Calculates the agency's differentiation relative to the specific field invited — not in abstract. Examines service overlap (full-service vs. specialists), sector coverage overlap, size asymmetry, and declared positioning. Outputs a differentiation index vs. each competitor: "You are highly differentiated from 2 of the 3 other agencies. Risk: Agency C has identical positioning to yours."

When the competitive field contains primarily specialists and the agency is a generalist (or vice versa), PitchWhich flags the structural asymmetry explicitly: "You are the only 360° agency in a field of four digital specialists. The brief requests TV and print alongside digital — this is your structural advantage." Or the inverse risk: "You are a generalist competing against sector specialists — risk of being perceived as less expert."

After a Go decision, PitchWhich generates a recommended proposal structure based on the brief type, sector, and decision-maker profile. For a procurement-led brief: front-load credentials and pricing clarity. For a CMO-led brief: front-load strategic thinking and the big idea. The structure adapts to who is reading, not a one-size-fits-all template.

The NLP analysis of the brief — extracted objectives, target audience signals, budget range (if stated), and stated decision criteria — is exported as a structured summary the agency can use directly in the "Understanding your brief" section of the pitch. The system has already read the brief carefully; the team doesn't need to rewrite what PitchWhich already extracted.

Studio-tier agencies create and save custom proposal templates by brief type (AOR, project, media-only, creative-only) and by sector (FMCG, financial services, public sector, technology). Templates carry the agency's standard sections, brand voice guidelines, and preferred case study format. Applied automatically when a matching brief arrives.

Pro tier includes 2 seats (CEO + one consultant or partner). Studio tier includes 5 seats. All users see the same pipeline, case study library, and score history. Comments and annotations on a brief are visible to all seats. Each user's research contributions are logged separately to avoid overwriting.

Per-tenant data isolation — no agency can see another's briefs, clients, or scores. Brief content is encrypted at rest. All data stored on EU-region servers. A standard Data Processing Agreement (DPA) is provided at signup. A metadata-only mode is available: score a brief by entering only structured metadata (sector, budget range, deadline) without uploading the brief text itself.

A documented REST API with standard endpoints: submit a brief for scoring, retrieve a score with factor breakdown, add a case study, retrieve matched case studies for a brief ID, and log a pitch outcome. Documented with a full OpenAPI spec. Enables integration with the agency's existing tools: project management systems, CRMs, or custom dashboards.

After 12 months of use, the scoring model adjusts its factor weights based on the agency's own win/loss history. If the agency consistently wins despite weak external signals — for example, succeeding with procurement-led briefs in a particular sector — the model learns this and re-weights accordingly. Shown explicitly: "Your model is calibrated to 18 historical outcomes from your agency."

The list of data sources linked in the research guide — financial registries, ad transparency libraries, company databases, trade press sources — is maintained and updated by PitchWhich as sources change, require new access methods, or as new transparency tools are launched. Users always have current, working source links, never broken ones from a guide updated 18 months ago.

The scoring interface, pipeline dashboard, and case study library are fully accessible on mobile. New brief alerts can be received via push notification. The go/no-go score for a brief can be viewed and shared directly from a phone. Pitch decisions often need to be made quickly — in transit, between meetings, on a call — not only at a desk.