Wealth & Advisory AI
P

PlannerPal

PlannerPal is a United Kingdom workflow platform for financial advisers, paraplanners and support staff, led by chief executive and co founder Mark Whitcroft. It records face to face, Microsoft Teams and Zoom client meetings without requiring a meeting bot, produces transcripts, and generates seven document types in the individual adviser's and firm's own style, including meeting notes, client emails, proposals, review letters, advice letters and suitability reports, with citations intended to evidence data completeness.

A customer relationship management updater proposes record changes back to the adviser's back office rather than writing them silently, and generated notes are cross checked against live data held in the connected system. In January 2026 it added a pre meeting preparation pack that assembles a firm defined client briefing from portfolio valuations, tax and allowance summaries, progress against goals, actions and communications since the previous meeting, compliance status and a personalized agenda, drawing on records, prior notes, uploaded documents and valuation statements.

The company positions this as addressing preparation rather than the post meeting administration that most competing tools target. Integration with intelliflo is direct and listed in that vendor's partner directory, and the models are described as trained specifically for the United Kingdom advice market so that product names and industry terminology are handled correctly. Reported usage covers more than three hundred and fifty firms, with average administration and post meeting time down fifteen to twenty percent.

Last VerifiedAugust 19, 2026
Compare PlannerPal with other vendors
Founded
2023
Headquarters
United Kingdom
Categories
wealth-and-advisory, compliance-and-surveillance
Assessment

Capability Axes

Capability grades

15 of 15 axes rated · 5 graded A or B

AI Capability
AI Centrality
AA on AI CentralityThe artificial intelligence is the product. Remove the models and there is nothing left to sell.
Vendor Published

Every output the product exists to produce is generated. Transcription of recorded meetings, seven document types written in the individual adviser's style, proposed record updates derived from conversation, and the pre meeting briefing synthesised from valuations, notes and correspondence are all model driven. Remove the models and what remains is a connector to a back office system and a recording function, which is not the product anyone is buying.

The company also states the models were trained specifically for the United Kingdom advice market so that product names and industry terminology are handled correctly, which is a model level investment rather than a configuration one.

Autonomy and Oversight Model
BB on Autonomy and Oversight ModelA written commitment that the models work alongside human judgment, with real review surfaces, short of the full control structure: commonly the threshold at which the system stops or what happens after it is wrong.
Vendor Published

Two real mechanisms sit in the path and neither is fully described. Generated advice letters, suitability reports and review letters carry citations, stated as evidencing data completeness and transparency, which surfaces where a claim in the document came from at the point the adviser reviews it. Separately, the CRM updater suggests changes to the back office rather than writing them automatically, so a person confirms each update to the client file.

Both are the right shape, but no confidence measure accompanies the citations, and nothing states what happens when source data is missing or contradictory. No escalation or threshold is described, and the extent of citation coverage across the seven document types is not set out.

Model Risk Management and Transparency
CC on Model Risk Management and TransparencyTransparency is claimed in general terms with no mechanism a model validator could interrogate.
Vendor Published

Accuracy is asserted and never measured. Marketing describes context aware accuracy and reduced errors through cross checking against live record data, but no error rate, evaluation methodology, test set or independent assessment is published for transcription, extraction or document generation. The citation feature evidences where information came from, which is provenance rather than a measure of how often the generated text is wrong, and the two are easily conflated.

Nothing describes how model changes are detected or managed over time. For a product whose output is the file evidence that advice was suitable, the absence of any published measure of error in either direction is the central gap.

Operational and Outcome Evidence
BB on Operational and Outcome EvidenceVendor aggregate claims with real figures, or audited scale disclosures from a publicly listed company.
Vendor Published

Traction is real and the figures are specific. Usage is reported across more than three hundred and fifty firms, with average administration and post meeting time down fifteen to twenty percent. Individual advisers report a seventy five percent reduction in client administration and thirty to sixty minutes saved per meeting.

Some of this is carried in interviews published by a major back office vendor rather than only on the company's own site, and independent trade press covered the pre meeting product launch, so the vendor is visible outside its own marketing. The weakness is attribution. No customer firm is named publicly, the adviser quotations are anonymous, and the percentage figures ultimately originate with the company even where a third party published them.

AI Safety and Data Stewardship
CC on AI Safety and Data StewardshipGeneral assurances that do not answer the question this axis asks, which is whether one customer’s data trains models serving its competitors. Unbounded cross client learning stated with no boundary grades here too.
Vendor Published

Neither half is addressed. Nothing published states whether recorded client meetings, transcripts or extracted financial circumstances are used to train or improve models. Nor does anything say whether third party providers are permitted to train on that material, whether a firm can decline such use, or how long recordings and transcripts are retained. At least two direct competitors publish an explicit position on exactly this question.

On the safety side there is no description of controls against fabricated content in documents that become regulatory evidence, no evaluation of output quality, and no account of what happens when source material is thin. The citation feature is the nearest thing to a control and it is aimed at completeness rather than correctness.

Regulatory and Compliance
GLBA and Data Privacy Posture
CC on GLBA and Data Privacy PostureA standard privacy policy that covers the website rather than the service, or silence on a product that touches limited consumer data.
Vendor Published

Nothing substantive on privacy is published for a product that records client conversations and holds synchronized client records. No privacy management certification is held, and no retention or deletion period is published for recordings, transcripts or generated documents. No position is stated on the lawful basis for processing recorded conversations or on consent from the client being recorded, which is the specific question a recording product raises.

The pre meeting pack also assembles a consolidated view of a client's holdings, tax position and communications history, and nothing describes how that assembled profile is stored or how long it persists.

Security Certifications and Trust Center
CC on Security Certifications and Trust CenterA single footer line, or certifications asserted without being enumerated, which is weaker than naming them because it invites an assumption a buyer cannot check.
Vendor Published

The public security surface is empty. No ISO 27001 certification or SOC attestation is published, and there is no trust center, security page or scope statement. This matters in proportion to what the platform holds, which is recordings and transcripts of client conversations plus synchronized client records drawn from a firm's back office system.

Advice firms in this market are increasingly asked to evidence supplier due diligence by their networks and compliance providers, and nothing published here would answer such a questionnaire. The direct integration with a major back office vendor implies that vendor conducted some assessment, but no detail of any such review is public.

Regulatory Status and Licensure
BB on Regulatory Status and LicensureThe regulatory position is clearly stated and appropriate to the product, with part of the verification left to the buyer.
Vendor Published

PlannerPal holds no financial license, which is correct for a technology supplier, and the regulatory grounding comes from what the product produces. Its outputs include suitability reports and advice letters, which are the mandated file level evidence that a recommendation was appropriate, and the pre meeting pack surfaces compliance status as a standard element. The models are built around the United Kingdom advice rulebook rather than adapted from another market.

No regulator has assessed, tested or supervised the product, and there is no authorization or program participation. The compliance framing is the vendor's own characterization rather than anything externally validated.

AI Governance and Bias Disclosure
CC on AI Governance and Bias DisclosureResponsible artificial intelligence committed to in policy language with no evaluation behind it, on a product whose bias surface is modest.
Vendor Published

Nothing published addresses whether the system performs evenly across the clients it processes. The exposure is specific rather than theoretical: the product converts recorded speech into the notes, letters and suitability rationales that document a client's circumstances, and transcription accuracy is a documented source of variation across accents, speech patterns and age. A transcription failure here does not merely produce a poor summary. It produces an inaccurate regulated record. No demographic analysis or evaluation of transcription performance across speaker populations is published. There is no bias testing methodology and no governance framework covering model fairness.

AI Liability and Recourse
CC on AI Liability and RecourseMechanisms that enable challenge, such as audit trails and source traceability, with nothing standing behind the output and no route for the person affected.
Vendor Published

The vendor publishes no accuracy guarantee, remediation commitment or allocation of responsibility between itself and the advice firm. The citation feature and the suggested rather than automatic record updates place the burden of verification on the adviser, which is appropriate for a regulated professional but creates no obligation on the supplier when a generated document misstates a client's circumstances.

The retail client is further removed again: their conversation becomes the transcript that becomes the suitability rationale, they are not told a model drafted it, and no route exists for them to establish how their circumstances were captured if the advice is later disputed.

Integration and Deployment
Model Supply Chain Disclosure
CC on Model Supply Chain DisclosureThe architecture is described and no provider is named.
Vendor Published

No base model, provider, version or component is named for transcription, generation or the CRM update suggestions. The company states the models are trained for the United Kingdom advice market, which describes tuning rather than origin and leaves open whether the underlying models are its own or licensed. No subprocessor list is published. There is no identifier a firm could log against a generated suitability report to establish later which system produced it. A competitor of similar size publishes a dated table naming every model and transcription provider, its processing activity and its country of processing.

Core Systems and Integration Depth
BB on Core Systems and Integration DepthNamed systems or a documented public API, with the depth or the production evidence left open.
Vendor Published

The integration is deeper than a data export and is externally corroborated. PlannerPal appears in the partner directory of the largest United Kingdom adviser back office system, with a direct connection. It syncs client lists and auto saves generated documents. It also enriches meeting notes with live record data and cross checks outputs against that data for context.

Meeting capture connects directly to the two main conferencing platforms without deploying a meeting bot into the call, which is a meaningful architectural difference for firms whose compliance teams object to third party bots joining client conversations. A competitor's published comparison credits it with four back office integrations. Only one back office connection is independently evidenced. No partner directory or public documentation covers the others, and there is no service status page or changelog.

Deployment Model and Data Residency
CC on Deployment Model and Data ResidencyCloud only with nothing stated, which is the category norm.
Vendor Published

Delivery is hosted software integrated into the firm's existing back office and conferencing tools. Beyond that, nothing is published. No hosting region or country is named and no in country residency option is described. No data transfer mechanism is set out, and no subprocessor list exists. The gap is material for a product capturing recordings of United Kingdom retail client conversations, where firms and their compliance oversight providers routinely ask where such recordings are held. A direct competitor publishes the processing country for each of its model and transcription providers, which shows the disclosure is achievable at this company size.

Commercial
Commercial Transparency
CC on Commercial TransparencyNo price is published and engagement runs through a demo form, which is the norm in this index.
Vendor Published

Pricing is not public. Rates, tiers and seat pricing are unpublished, as are volume bands and trial terms. Distribution through a major back office partner store might imply standardized commercial terms, but that listing carries no figure or structure either. Every route resolves to a demo or contact request. At least two direct competitors publish either a per seat monthly rate or a public pricing page, so the absence here is a choice rather than a category norm.

Institution and Segment Coverage
CC on Institution and Segment CoverageSegments claimed broadly, banks, fintechs, credit unions, without evidence any of them has its own maintained surface.
Vendor Published

Coverage is narrow, not merely undocumented. The product serves one jurisdiction and one buyer type, United Kingdom financial advice firms, with roles inside those firms spanning advisers, paraplanners and support staff. Penetration within that segment is real at more than three hundred and fifty firms, but penetration is not breadth. There is no deployment outside the United Kingdom and no adjacent institution type such as banks, discretionary managers or insurers. The model training and document formats are built around a single national rulebook that would require rebuilding for any other market.

Alternatives to PlannerPal

Vendors in the same categories as PlannerPal whose profiles look most alike, each with a line on what the product does. Similar is not the same: check that each one does the job you need. No vendor pays for placement.

Ammonite AI is a United Kingdom company selling Planbot, a report drafting tool for financial advice firms, launched in February 2026.

Catchlight sells prospect prioritization to wealth management firms, answering two questions an adviser faces every morning: which prospect to call next, and what that person is likely to care about.

Sherpas sells AI agents that do the analytical work behind financial advice for wealth management firms.

HyperNorm AI sells an AI decision platform to registered investment advisers, wealth managers, fund managers and fintech platforms.

Timeline sells an integrated ecosystem to United Kingdom financial advice firms that spans three businesses sold as one proposition.

WNSTN sells an AI assistant and analytics layer that brokerages and other investing platforms embed in their own apps.

The index publishes no overall score or ranking. See what we assess and the methodology.

Commercial

Pricing

Vendor-published figures are labeled as such. Figures labeled “Estimated” are derived from third-party sources and have not been confirmed by the vendor.

No pricing data has been verified for this vendor. Pricing information will be published here once confirmed through vendor disclosure or third-party estimation.

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AI FinTech Index

The AI FinTech Index is an independent index that tracks changes to AI vendors in financial services. It holds 610 vendors across banking, lending, insurance, wealth, capital markets and financial crime compliance, each graded on the same 15 capability axes from public sources. No vendor pays for inclusion, placement, or rating.

Index Status
Last index update
October 20, 2026
The AI FinTech Index is an editorial reference, not a regulatory body. Vendor data is verified against published sources and public regulatory filings. Figures labeled “Estimated” have not been confirmed by the vendor. See the Methodology page for evaluation standards and limitations.
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