Data and research capabilities, built to your specification

We work as a research partner to commercial leadership, translating a written definition of your target market into a structured, verified and defensible view of it. Firmographic, technographic and organisational signals are researched, triangulated and reconciled against primary sources, then maintained on an agreed revalidation cycle so the picture stays current as markets move.

• Market sizing and territory planning built on lists nobody can trace back to a source
• Coverage gaps that surface only after quota and headcount have been committed
• Buying committees mapped to job titles rather than to actual decision authority
• Silent record decay that erodes targeting accuracy quarter after quarter

• Sizing and segmenting a new region, vertical or product line before investment
• Rebuilding an addressable market universe ahead of annual planning
• Mapping buying committees for named enterprise account programmes
• Establishing a governed source of truth that feeds CRM, marketing and analytics

• Technology
• Software & SaaS
• Cyber Security
• Manufacturing
• Consulting

• Documented ICP and field specification agreed before production
• Verified account and contact universe mapped to your CRM schema
• Coverage and confidence reporting by segment, region and field
• Scheduled refresh cycles with change logs between deliveries

• Research methodology and evidence trail behind every record, not a repackaged catalogue
• Coverage measured against your written ICP rather than a vendor's inventory
• Human verification applied where automated sources cannot be trusted
• Delivery shaped to the systems and governance standards you already run

1. 01Requirement and ICP definition workshop
2. 02Source strategy and universe construction
3. 03Multi-stage verification and quality scoring
4. 04Delivery, integration support and refresh cycles

Event programmes represent significant, non-recoverable investment decided months in advance. We research the full ecosystem around each event — who exhibits, who sponsors, who speaks and which organisations they represent — and reconcile it against your priority accounts, so sponsorship, staffing and meeting decisions are made on evidence rather than on the published agenda alone.

• Sponsorship and floor-space decisions taken without visibility of who will actually attend
• Limited on-site meeting capacity allocated on availability rather than account value
• Event ROI that cannot be evidenced because no target list existed beforehand
• Follow-up momentum lost in the weeks after a show closes

• Evaluating which events in a global calendar justify continued investment
• Building pre-show target lists and meeting agendas for field teams
• Competitive monitoring of exhibitor and sponsor presence across a category
• Structuring post-event follow-up around verified organisational context

• Technology
• Artificial Intelligence
• Cyber Security
• Healthcare
• Events & Conferences

• Event ecosystem profile covering exhibitors, sponsors, speakers and represented organisations
• Priority-account match report with recommended meeting targets
• Pre-show outreach file in your preferred format and schema
• Post-event consolidation and follow-up dataset

• Research conducted per event rather than resold from a static directory
• Organisational context attached to every profile, not just names and titles
• Delivery timed to your planning and travel deadlines
• Consistent standards applied across regions and event calendars

1. 01Event calendar and objective review
2. 02Ecosystem research and verification
3. 03Priority-account matching and prioritisation
4. 04Pre-show delivery and post-event consolidation

Syndicated research is written for a general audience and rarely answers the question in front of you. We scope research to your specific decision — category structure, vendor positioning, adoption signals, pricing posture and structural shifts — and deliver findings with the evidence trail intact, refreshed on a cadence that matches your planning cycle.

• Strategy and product decisions informed by reports that are months out of date
• Vendor landscapes that omit the regional or emerging players that matter most
• Conclusions presented without sources that stand up to executive scrutiny
• Internal teams lacking capacity to run structured market research alongside delivery work

• Assessing entry into a new category, region or adjacent market
• Preparing board, investment committee or diligence material
• Competitive and positioning reviews ahead of product or pricing decisions
• Tracking adoption and consolidation across a category over time

• Technology
• Software & SaaS
• Artificial Intelligence
• Consulting
• Marketing Agencies

• Written research brief with agreed scope and success criteria
• Category landscape and vendor mapping with positioning analysis
• Adoption and signal analysis supported by cited sources
• Executive briefing document and analyst review session

• Every finding traceable to a documented source
• Scope defined around your decision, not a generic market segment
• Analyst judgement applied on top of collection, not in place of it
• Refresh cadence aligned to your planning and reporting calendar

1. 01Research question and scope definition
2. 02Source mapping and structured collection
3. 03Analyst synthesis and triangulation
4. 04Briefing, review and scheduled refresh

Model performance is constrained far more often by data quality than by architecture. We design datasets to a written schema and annotation standard, apply human review at defined checkpoints, and document provenance and methodology so the work withstands technical, procurement and compliance review before it ever reaches a training run.

• Engineering time consumed by cleaning, labelling and reconciling source data
• Inconsistent annotation caused by informal or undocumented guidelines
• Provenance questions from legal, procurement or risk that nobody can answer
• Evaluation sets that leak into training data and distort measured performance

• Building training and fine-tuning corpora for domain-specific models
• Creating held-out evaluation and benchmark sets with version control
• Structuring unstructured business content into model-consumable formats
• Supplementing internal data where coverage or class balance is insufficient

• Artificial Intelligence
• Technology
• Software & SaaS
• Healthcare
• Cyber Security

• Written schema, taxonomy and annotation guidelines
• Structured datasets with versioning and change history
• Quality assurance report covering sampling, agreement and error rates
• Provenance and methodology documentation for internal review

• Research discipline applied to dataset construction, not bulk collection
• Human review embedded in the pipeline at defined checkpoints
• Documentation prepared for procurement, security and compliance scrutiny
• Delivery formats matched to your training and evaluation infrastructure

1. 01Schema, taxonomy and label design
2. 02Collection, structuring and annotation
3. 03Human-in-the-loop quality assurance
4. 04Versioned delivery and iteration cycles

Most organisations do not have a data volume problem; they have a data confidence problem. We audit the records you hold, quantify where accuracy has degraded, complete missing fields against verified sources, resolve duplicates and normalise values to your taxonomy — returning records that reconcile cleanly with the systems and reports built on top of them.

• Automation and routing rules failing silently on incomplete records
• Duplicate and conflicting records distorting pipeline and attribution reporting
• Inconsistent taxonomies preventing reliable segmentation and analysis
• Steady record decay treated as an occasional clean-up rather than a managed cycle

• Preparing CRM data ahead of a migration, consolidation or platform change
• Restoring confidence in reporting before annual planning or board review
• Completing fields required for territory design, routing and lead scoring
• Establishing a recurring enrichment cycle to hold accuracy steady

• Software & SaaS
• Technology
• Retail & eCommerce
• Manufacturing
• Marketing Agencies

• Data audit report quantifying completeness, duplication and decay
• Enriched and normalised records aligned to your taxonomy
• Deduplication and merge log with applied match logic
• Re-import ready files and an agreed refresh schedule

• Verification against primary sources rather than automated inference alone
• Match and merge logic documented and reviewable, not a black box
• Taxonomy and field standards agreed with your operations team up front
• Decay managed on a schedule instead of addressed reactively

1. 01Record audit and gap analysis
2. 02Enrichment, matching and verification
3. 03Deduplication and normalisation to your taxonomy
4. 04Re-import support and recurring refresh

Some decisions depend on questions that cannot be resolved by any product on the market. We scope those assignments as structured research engagements: a written brief, an agreed source strategy, defined milestones and a named analyst team accountable for the outcome — giving you senior research capacity without adding permanent headcount.

• Strategic questions repeatedly deferred because no internal team has capacity
• Off-the-shelf datasets that cover the market but not the specific question
• Research effort duplicated across teams with no shared standard or output format
• Findings that arrive too late to influence the decision they were meant to inform

• Deep-dive account, supplier or partner research ahead of a major engagement
• Market entry, expansion and white-space assessments
• Competitive, pricing and go-to-market investigations
• Ongoing research retainers supporting strategy and corporate development

• Consulting
• Technology
• Artificial Intelligence
• Healthcare
• Manufacturing

• Written brief with scope, sources, milestones and success criteria
• Structured research findings with full source citation
• Analyst commentary and recommendations in your preferred format
• Review session and optional ongoing research cadence

• Senior analyst judgement applied to ambiguous, high-stakes questions
• Scope, milestones and deliverable format agreed before work begins
• Confidentiality and discretion treated as a condition of engagement
• Research capacity that scales with demand rather than headcount

1. 01Brief definition and success criteria
2. 02Source strategy and research design
3. 03Analyst research, verification and triangulation
4. 04Deliverable, review and follow-on scope

Accuracy claims are easy to make and rarely tested. We design a sampling methodology appropriate to your dataset, validate records against primary sources, and report measured accuracy by field, segment and supplier — producing an evidence base your teams can use in vendor reviews, governance forums and internal quality programmes.

• Vendor accuracy claims accepted without independent measurement
• No repeatable method for assessing quality across suppliers or time
• Quality issues discovered by commercial teams rather than by governance
• Governance and compliance stakeholders requesting evidence that does not exist

• Benchmarking data suppliers during selection or contract renewal
• Establishing a recurring internal data quality measurement programme
• Validating a dataset before it is used for planning or model training
• Providing evidenced accuracy reporting to governance and compliance

• Cyber Security
• Healthcare
• Software & SaaS
• Manufacturing
• Consulting

• Documented sampling methodology and verification criteria
• Field-level accuracy scoring by segment, region and source
• Findings report identifying weak fields and remediation priorities
• Optional recurring verification cycles with trend reporting

• Independent position with no interest in the result of the assessment
• Verification against primary sources rather than cross-referenced vendors
• Methodology transparent enough to be repeated and audited
• Findings framed as actionable remediation, not just a score

1. 01Sampling design and criteria agreement
2. 02Primary-source verification
3. 03Accuracy scoring and analysis
4. 04Reporting, recommendations and re-testing

Large organisations frequently run data and research across several suppliers, each with its own schema, methodology and quality standard. We consolidate that activity into one governed programme with a named engagement lead, shared definitions, agreed service levels and reporting that spans every workstream, region and business unit.

• Fragmented supplier landscape producing incompatible outputs and duplicated spend
• No consistent definition of quality across regions or business units
• Procurement, security and compliance review repeated for every new supplier
• Limited visibility of what is being delivered, by whom, at what standard

• Consolidating multiple data and research suppliers into one programme
• Standing up a global research capability across regions and business units
• Running continuous intelligence programmes alongside project-based research
• Establishing shared data standards and governance across commercial functions

• Technology
• Consulting
• Manufacturing
• Healthcare
• Cyber Security

• Programme design covering scope, governance, standards and service levels
• Consolidated schema and quality framework applied across workstreams
• Coordinated delivery calendar with a single point of accountability
• Quarterly performance review and optimisation reporting

• One accountable partner across every workstream and region
• A single documented quality standard rather than supplier-by-supplier variation
• Procurement, security and compliance completed once and reused
• Programme governance designed for enterprise reporting requirements

1. 01Programme design and governance definition
2. 02Workstream onboarding and standard setting
3. 03Managed delivery under agreed service levels
4. 04Quarterly review, reporting and optimisation