AI Business Results in 30 Days

Your principal consultant leads from scoping to go live. They are a hands on growth marketer with deep digital marketing and product growth experience. They run weekly reviews, own the pilot plan, and stay involved through the first live week so there is no handover gap.

Small and senior by design. Delivery is done by the principal consultant with support from a solutions engineer for integrations and data plumbing. No junior handoffs. If a specialist is needed, for example telephony or BI, we bring them from a trusted pool of specialists.

We focus on one measurable outcome, ship a real agent in 30 days, and keep ownership with you. Vendor neutral model choices, clear success thresholds, and weekly checkpoints reduce risk and keep the work tied to ROI. You keep the accounts, data, and configurations.

We are based in London and work primarily in UK and EU hours. For North America we run early or late windows for key sessions, and we provide async updates so decisions never wait on time zones.

Yes. We are comfortable with mutual NDAs, DPAs, and standard security questionnaires. We deploy in your accounts when possible, minimise retention, and document data flows for review. If your process requires a short DPIA, we will work with your team to complete it.

Success is a pilot that moves one KPI and de-risks scale. Typical examples include higher qualified lead rate, lower first-response time, ticket deflection with high accuracy, or faster sales follow-ups. We set a baseline in week 1, track weekly, and only recommend expansion after thresholds are met.

Absolutely. We offer a 25 minute intro with the principal consultant to align goals and constraints, followed by a short walk through of a 30 day project tailored to your process. You will see relevant work, discuss risks and guardrails, and leave with a one page plan and a clear go or no go decision path.

Strategy, implementation, and enablement. We scope a single high value use case, build a working agent, connect the necessary data sources, and instrument measurement. After launch we train your team and hand over playbooks so you own it.

Revenue and service workflows. Typical wins include lead capture and qualification, sales follow ups, support deflection and triage, and marketing production QA. We pick one process and one KPI, prove lift, then expand.

Your side provides access, a weekly reviewer, and sign-offs on tone and risk. Our side handles plan, build, integrations, guardrails, and measurement. The principal consultant leads delivery end to end. A solutions engineer supports integrations and data plumbing.

One process, one channel, and the minimum viable integrations. We ignore long tail intents and custom platform features. Anything that does not move the agreed KPI is parked for the scale phase.

By the end of Week 2 you see a working prototype using your content and an evaluation set with early accuracy results. This confirms tone, knowledge coverage, and the feasibility of the KPI target.

Start read only, connect only the essentials, and document every action. Typical order is knowledge sources, CRM or help desk, then one optional enrichment source. Write actions are deferred until quality passes and owners are ready.

We use a small evaluation set from your real conversations, add policy and tone rules, and run reviewer checks on a random sample. Low confidence routes to human agents. We only go live in a limited slice after thresholds are met.

We enable the agent in one channel and a small audience slice. We watch KPI trend, safety KPI, and example transcripts each day. Handoffs to humans are enabled and audit logs are on. If results hold for several days, we expand coverage.

A short decision log captures scope choices, guardrails, and rollbacks. Security items are reviewed in Week 1 and Week 3. Any high-risk intents require explicit human approval in the workflow. We keep a rollback plan ready for the live slice.

You receive prompts, configs, code, architecture notes, an evaluation set, and a simple dashboard. We run a one hour enablement session and provide playbooks for updates and escalation. If you want help to scale, we can continue on a light retainer or a fixed implementation.

Yes. We can work under your (NDA) Non Disclosure Agreement and a (DPA) Data Processing Agreement that reflects your roles and processing needs. For pilots we prefer processing as a processor in your environment with minimal retention. We document categories of data, purposes, retention, and deletion triggers in a short appendix.

By default in your accounts and regions. If you use Azure OpenAI or AWS Bedrock we follow your tenancy and region selection, typically UK or EU for EU/UK customers. If a third party service is required, we list it, pin its region where possible, and route only the minimum necessary data.

Yes. Snowflake, BigQuery, and Redshift are typical. For pilots we use read only service accounts and parameterized queries. One to two days covers credential setup, role scoping, and the first report or retrieval view.

Twilio and WhatsApp Business Platform are supported for notifications and simple flows. Voice is possible after quality is proven in a safer channel. Plan on two to four days for a basic pilot, including template approvals for WhatsApp if needed.

Confluence, SharePoint, Google Drive, Notion, and sitemap based crawls. We use read only access and maintain a source of truth index with freshness checks. Setup is usually one to two days for credentials, scopes, and an initial sync.

Shopify and Stripe are common for order lookup and policy answers. We keep pilots read only to start. Basic connections take one to two days, including field mapping for order status, refunds, and shipment data.

We use your SSO where available and scoped service accounts when not. Permissions are least privilege, with separate admin and runtime credentials. Most setups fit within the first week alongside other integration work.

Start with knowledge and one system of record, for example CRM or help desk. Add one enrichment or messaging tool if needed. Writes are gated until the prototype passes accuracy checks. This keeps momentum without risking data quality.

We evaluate the API and authentication model during Week 1. If it is a modern REST or GraphQL API, a basic read only connector is usually feasible in a few days. If not, we propose an alternative path, for example a warehouse view or export import, so the pilot can proceed.

Start with the job to be done, risk posture, and cost. We shortlist two models, run a small eval set from your real data, and compare accuracy, refusal behavior, latency, and price per successful task. The winner is the model that hits the KPI at acceptable cost and risk, not the one with the biggest benchmark score.

When security reviews, data residency, or procurement speed matter more than marginal quality differences. Azure OpenAI and Bedrock simplify enterprise controls and regional placement. If your team already uses either platform, we default to it for faster approval.

Yes. We isolate prompts, retrieval, and tool logic from the underlying model. We keep a thin adapter layer so you can swap GPT, Claude, or Gemini with minimal refactoring. This is part of handover so your team can change later without a rewrite.

We design for “price per solved task,” not tokens alone. Techniques include smaller context windows via retrieval, response compression, and caching for repeat prompts. We test multiple temperature and system prompt variants to reach accuracy with fewer retries.

Yes, when data sensitivity, latency, or cost require it. We will scope a small self hosted track if your infrastructure allows it, usually for narrow tasks like classification, extraction, or translation. We only propose self hosted generation when your team can own the MLOps reliably.

We protect you with two mechanisms. An abstraction layer that lets you switch providers, and a lightweight offline eval set you can rerun when a new model ships. If a newer model wins on quality or cost, you have the evidence to switch confidently.

Keep it simple. Retrieval for knowledge, tools for actions, evaluator checks for quality. We use your platform of choice for hosting and secrets. If you already run Azure or AWS, we fit the design to native services for logging, access control, and monitoring.

Yes. Use a strong model for user facing generation and a lighter model for classification, routing, or summarisation. This reduces cost without hurting quality. We document which task uses which model and the fallbacks if a call fails.

We include sensitive and tricky prompts in the eval set, then score for appropriateness, groundedness, and refusal correctness. We add policy prompts and guardrails. Low confidence or policy sensitive cases route to a human by design.

A short comparison table with KPI accuracy, latency, and cost per successful task, plus a few side by side examples and failure cases. We recommend a primary model and a backup. You keep the eval set and scripts so you can rerun the test later.

The principal consultant remains your single owner for the first 90 days. A solutions engineer joins as needed for integrations and analytics. You will always have one Slack or email thread for fast issues and a monthly success review on calendar.

Business hours coverage in UK/EU time. Initial response within 1 business day for standard items, within 4 hours for project blocking issues. Workarounds shared first, fix or action plan next business day. Higher touch options are available if you need them.

A before and after chart on the primary KPI, a short write up of scope and guardrails, side by side examples of agent outputs, and a recommendation to scale or stop. You also get a measurement workbook so finance can re run the numbers.

Typical pilots show one or more of the following on a narrow scope: +20–40 percent qualified bookings on high-intent pages, 15–30 percent faster first response, 10–25 percent accurate deflection on top intents, or 20–40 percent reduction in drafting time for standard replies. Results depend on content quality and process fit.

Use a holdout control group or phased rollout when traffic allows. Where that is not possible, we use time series controls, pre registered thresholds, and reviewer scoring on a random sample. We avoid vanity metrics and only claim wins where a counterfactual, what would have ahppended if we hadn't made the change, is credible.

Access to the KPI source of truth, recent volumes by channel, a short list of top intents, and any seasonality flags. For sales impact we need conversion steps and meeting outcomes. For support we need ticket reasons, CSAT, and handle time.

We choose a threshold that is both meaningful and achievable within 30 days. Example: reduce time to first touch from 4 hours to under 30 minutes during business hours, or reach 80 percent correct deflection on the top 20 intents at stable CSAT. If we do not meet the threshold, we do not recommend scale.

We combine time saved per interaction, hourly fully loaded cost, and the portion of work reliably automated. For example, 30 seconds saved on 40,000 monthly replies at an effective cost of £25 per hour is roughly £8,300 per month before quality adjustments.

You get a simple dashboard with volume, the primary KPI trend, safety KPI, and examples. We review weekly with a short written summary that finance and leadership can read in less than five minutes.

We make a clear call. If the primary KPI improves and safety holds, we propose a scale plan. If results are flat, we either tighten scope for one more iteration or stop and share what we learned. No long contracts are required to make that decision.

Fixed fee. The price covers scoping, a working agent in one channel, essential integrations, an evaluation set, guardrails, a simple dashboard, and weekly reviews. You keep prompts, configs, and code. There are no usage mark ups and no platform lock in.

Scope, complexity of integrations, data sensitivity, and review requirements. A read only CRM and one content source is a lighter lift than bi directional writes and multiple systems. We share a one page scope before you sign so the fee is predictable.

If you use paid APIs or cloud services, those pass through at your rates. We do not resell usage. Many clients already have Azure OpenAI or Bedrock set up, which keeps procurement simple.

Discovery, architecture outline, agent design and build, integrations to the minimum viable tools, evaluation and QA, stakeholder training, a results readout, and handover materials. Minor tweaks during the pilot are included, major scope changes are not.

Multi region rollout, large scale content overhauls, deep data modeling, or building bespoke internal tools. If those become necessary we price them as follow on work after the pilot has proven value.

Yes. Two common options. An enablement retainer for monthly optimisation and training. A delivery retainer for continued build out of channels, intents, or integrations. Both have clear hour bands and outcomes so there are no surprises.

Yes. If your roadmap is clear, a fixed implementation works well. We define milestones, acceptance criteria, and a payment schedule linked to deliverables.

For 30 day projects, 50 percent on kickoff and 50 percent on week three. For retainers, monthly in advance. For fixed implementations, a milestone schedule. Invoices are in USD by default. We support standard PO processes.

We will not push scale if results are weak. You still keep the assets, documentation, and learning. If we recommend a second attempt, we propose a tighter scope with a reduced fee where appropriate.

Yes. We are comfortable with your (MSA) Master Services Agreement, (DPA) Data Processing Agreement, and security questionnaire. Pricing does not change for standard terms. If legal review introduces new obligations that affect scope or cost, we flag them before kickoff so you can decide.

Four clear weeks. Week 1 scope, access, baseline, and target KPI. Week 2 prototype with a small evaluation set and tone rules. Week 3 essential integrations, guardrails, and human in the loop. Week 4 hardening and a limited go live in one channel. You get a working agent, a dashboard, and a short results readout.

One primary KPI and one safety KPI agreed on day one. Examples. Lead gen, increase qualified bookings on high intent pages. Support, accurate deflection on the top 20 intents at stable CSAT. Sales, reduce time to first touch under 30 minutes during business hours. If the primary KPI improves and the safety KPI holds, we recommend scale.

No. We do not use customer data to train public models. When we use managed providers, we select settings that disable training on your prompts and completions. Retrieval indexes and logs stay in your accounts unless you ask otherwise.

(TLS) Transport Layer Security in transit, encryption at rest, and least privilege access by default. We use scoped service accounts, rotate credentials, and keep audit logs on. For prototypes we start read only and only enable write actions after owner approval.

Yes. In pilots we minimise what is processed, tag personal data where present, and make deletion workflows explicit. If you run a DPIA, we contribute architecture notes, data flows, and risk mitigations so your privacy team can sign off.

Vendor neutral, we choose the platform that fits your risk posture and region, often Azure OpenAI or AWS Bedrock for enterprises. We document the model, endpoints, regions, data handling settings, and fallbacks. If you prefer self hosted, we adapt the architecture accordingly.

The minimum to meet the KPI. Typically read only access to a content source and a test inbox or chat widget, plus a sandbox API key for CRM or help desk. We use SSO where available and separate admin from runtime credentials. Access is removed at pilot close.

Yes. We keep a decision log, enable request/response logging in your environment, and add evaluator checks and human approval for sensitive actions. Low-confidence cases route to agents with full context, and we keep rollback steps ready for the go live slice.

When everything runs in your stack there are no subprocessors from us. If we must use a third party for a pilot step, we list it in the DPA annex with purpose, data types, and region, and use settings that disable training and limit retention. You approve before use.

Salesforce and HubSpot are the most common. We start read only for lead and account lookups, then enable safe writes like notes, tasks, and status updates after review. Typical effort is one to two days for OAuth setup, field mapping, and a small test flow.

Zendesk and Intercom are standard, with Freshdesk and ServiceNow also supported. We begin with ticket search and metadata reads, then trial draft reply assist and tag updates. Expect one to three days to wire a pilot with logging in your environment.

A short plan, a working agent in one channel, essential integrations, an evaluation set, guardrails, and a simple dashboard. Weekly checkpoints, a go live in a controlled slice, and a one page results summary. You keep all configs, prompts, and code.

Enterprise wide rollout, complex multi region routing, deep data modeling, or long tail intent coverage. Those come after the pilot proves value. We keep scope tight so you see results quickly.

We are vendor neutral by default. We work in your cloud and tools whenever possible, or on trusted platforms such as Azure OpenAI or AWS Bedrock if speed and compliance benefit. You retain ownership of accounts and data.

We connect to the minimum viable set for the pilot. Commonly CRM, help desk, and a content source or data warehouse. We start with read only access, add writes where needed, and document every action with audit logs.

We use an evaluation set from your real conversations and content, add tone rules, and run reviewer checks for sensitive intents. Low confidence cases hand off to humans. We only scale after the agent meets agreed accuracy and CSAT targets.

Start from goals, risk posture, and cost. We evaluate mainstream models such as GPT, Claude, and Gemini alongside self hosted options if required. Retrieval for knowledge, tool use for actions, and clear guardrails keep behavior consistent.

Week 1 scope and access, week 2 prototype and evaluation, week 3 integrations and guardrails, week 4 hardening and limited go live. One standing review per week, short async updates, and quick decisions to maintain pace.

We agree the scale plan: more intents, more channels, or deeper integrations. We set a monthly cadence for optimisation, content updates, and reporting. You can keep us for enablement and heavier lifts, or run it in house with our handover.

Start with a focused website assistant that greets visitors, asks 2–3 qualifying questions, and routes hot prospects to booking. Data flows to your CRM (e.g., HubSpot or Salesforce) with UTM capture and dedupe. Success is measured on booked meetings and MQL quality, not chat volume. We target +20–40% more qualified bookings in the pilot, with guardrails for tone and compliance. Week 2 you see a working prototype, week 4 a controlled go live on high intent pages.

The play is instant triage and tailored first replies. An agent enriches new leads (Clearbit/LinkedIn/SERP), drafts context-aware emails, and nudges AEs with next best action inside the CRM. SLAs and ownership remain with the rep. We track time to first touch, reply rate, and meetings set. Typical pilot outcome is moving first touch to <10 minutes during business hours and lifting reply rates by 15–25%.

A monthly optimisation loop. We review KPI trend, accuracy sample, and backlog, then ship small improvements. You get a short written summary with decisions, owner names, and target dates so momentum does not stall.

One lightweight tracker with two lanes: reliability/bugs and improvements. You can submit directly from Slack or email. Each item has severity, owner, ETA, and status so stakeholders can see progress without chasing.

A one hour live handover, a short playbook for common updates, evaluator prompts for QA, and “cheat sheets” for agents and managers. Optional office hours for the first month to embed habits.

We keep the pilot KPI as the headline metric and add a safety KPI. Dashboards track volume, accuracy, response time, and examples. The monthly review decides scale, pause, or course correct based on data, not anecdotes.

You can update content directly through your sources, or we can run a monthly “freshness sweep.” The agent shows stale content warnings, and we keep a change log so marketing, support, and sales know what moved.

We run a small offline eval before upgrades, then a guarded rollout. If KPIs dip, we auto rollback and share a post mortem within two business days. You keep the eval set to rerun tests independently.

Yes, but we add them deliberately. We extend the evaluation set to the new channel or language, set fresh thresholds, and roll out in a limited slice. The success cadence stays the same so quality does not regress.

Two common paths. Enablement retainer for monthly optimisation and training, or fixed-scope work for larger expansions (new intents, deeper integrations, new channels). Either way, ownership of accounts, data, and configs remains with you.

Side by side examples of agent responses with sources, call or email summaries with next steps, evaluation set scores, and a simple KPI trend. You also see handoff transcripts that show how low confidence cases escalate to humans.

Two roles and a light cadence. A sponsor for quick decisions and a subject-matter reviewer for one hour per week. Access to content sources, a test inbox or chat widget, and read only CRM or help desk. We provide the plan, build, and measurement.

In. One channel, one process, a handful of integrations, an evaluation set, quality checks, and a small production slice. Out. Multi region rollout, long tail intents, deep data modeling, and custom platform features. Those can come after a positive result.

Guardrails and reviewers. Retrieval from approved sources only, policy and tone prompts, evaluator checks on a random sample, and human approval for sensitive actions. We deploy in your accounts where possible, with minimal retention and audit logs.

The minimum to move the KPI. Approved knowledge sources, a short list of top intents, access to a test channel, and read only connections to CRM or help desk. For sales or support, recent examples help us build an evaluation set quickly.

A/B or holdout control group if traffic allows. If not, a phased rollout with pre-registered thresholds and weekly trend checks. We document the counterfactual and include raw counts so finance can re run the math.

You get a one page results summary, a decision on scale or stop, and a clear plan. If we scale, we expand intents and channels or deepen integrations with the same measurement cadence. You keep prompts, configs, and code, and we can continue on a retainer or a fixed implementation.

Week 1 scope, access, baseline, and target KPI. Week 2 prototype plus an evaluation set and tone rules. Week 3 essential integrations, guardrails, and human in the loop. Week 4 hardening and a limited go live in one channel. One weekly review and short async updates keep decisions moving.

A sponsor for quick decisions, one subject-matter reviewer for one hour per week, access to approved knowledge sources, a test channel (chat or shared inbox), and read-only CRM or help desk credentials. If security requires it, we work in your cloud accounts.

We prioritise a narrow segment, generate personalised openers from firmographics and recent signals, and auto draft follow ups based on reply intent. Guardrails keep messaging on brand, and anything low confidence is flagged for human edit. We integrate with your sequence tool and log outcomes back to the CRM. Pilot success = more positive replies and show rates, not just send volume.

We standardise briefs, build a tone/style rubric, and create a “first draft factory” for core formats like landing pages, ads, and product emails. An evaluator prompt checks for accuracy, claims, and brand terms; anything risky is routed to a reviewer. We measure draft throughput, editorial time saved, and performance (CTR/CVR) on a small test set before scaling.

Start with the top 20 intents. Use retrieval over your help content plus order/account lookups. The agent handles straightforward cases, triages the rest with full context, and escalates on low confidence or high value. We track deflection rate, time to first response, and CSAT, aiming for accurate deflection without hurting satisfaction. Handoffs include clean summaries for agents.

We build agent “assist.” For every ticket, the assistant suggests a response, cites sources, and offers next steps or macros. Agents remain in control. We measure handle time, reopen rate, and QA scores. The pilot runs on one queue and one channel first, with HITL checks and rollback options.

Call and email summarisation with next steps. The agent ingests meeting recordings or threads, extracts key points, risks, and action items, and updates CRM fields reliably. Reps get concise notes and tailored follow ups. We measure CRM hygiene, follow up speed, and opportunity progression on a small cohort before scaling.

We flag risk early and prompt proactive outreach. The agent scans support patterns, product usage dips, and sentiment, then drafts tailored check ins and renewal nudges. CSMs review and send. We start with one segment or product line, measure touch coverage, response rates, and renewal probability, and expand after a positive lift.

Yes, but we introduce them carefully. For voice we use high accuracy transcription and a constrained intent set; for WhatsApp we respect template rules and session windows. The first pilot channel is usually web or email to lock quality, then we add voice/SMS/WhatsApp once KPIs hold. Success is response speed, accurate routing, and booked outcomes, not message volume.

Start with the top intents that block conversion and support, such as sizing, shipping, returns, and stock. Connect product catalog, order status, and policies. Launch on high intent PDPs and checkout help. Measure add to cart rate, conversion, and deflection. Target a controlled go live in week 4 with A/B coverage limited to 10 20 percent of traffic.

Focus on triage and accurate answers for the top 20 tickets. Use retrieval over docs and release notes, plus read only account lookups. Success is lower time to first response, shorter resolution time, and steady CSAT. Start with one queue and one channel, then expand after hitting the accuracy threshold you set.

On the demand side, qualify buyers and route them to the right listings. On the supply side, guide sellers on listing quality, pricing, and policy checks. Connect CRM, listings DB, and messaging. Pilot KPI is qualified inquiries per listing and faster resolution of routine questions. Escalations go to human agents with full context.

Keep scope narrow and auditable. Retrieval over approved content only, strict policy prompts, and human review on sensitive intents. Deploy in your environment and log all actions. KPIs are first response time and correct routing, not broad automation. Proceed to any account actions only after compliance signs off.

Restrict the project to non PHI workflows such as education, navigation, and intake pre screeners. Use retrieval of approved materials and clear disclaimers. Encrypt data, minimise retention, and keep humans in the loop for anything clinical. Success is faster routing and reduced admin load, with zero PHI processing in the pilot unless formally approved.

Cover availability, bookings, changes, and policy questions. Connect inventory and reservation systems in read only mode first. Launch on web chat or email for accuracy, then consider WhatsApp for itinerary nudges after results are stable. KPIs are conversion on direct booking flows and reduced handle time for common changes.

Unify store info, inventory visibility, and order status. The assistant answers product and policy questions, checks local stock, and directs to (BOPIS) Buy Online, Pick Up In Store where it helps conversion. Pilot on web chat and email, measure conversion, store contact reduction, and deflection. Expand to voice only after quality holds.

Lead qualification and proposal support. The assistant gathers context, drafts a scoped response, and books discovery. Retrieval uses your case notes, playbooks, and rate cards. KPI is qualified meetings set and faster proposal turnaround. Start on the website and shared inboxes, then add CRM automations.

Focus on admissions and student support. Connect program info, deadlines, and FAQs. Provide clear next steps and escalate finance or visa questions to humans. KPIs are inquiry to application rate and deflection for routine queries. Launch during a single intake window to show impact quickly.

Begin with order status, delivery windows, and documentation guidance. Connect (TMS/ERP) Transportation Management System/Enterprise Resource Planning in read only mode and restrict to a narrow set of intents. Provide clear confidence indicators and human escalation for exceptions. Measure fewer status tickets, faster turnaround on routine queries, and agent time saved. Move to write actions only after gate reviews.

We agree one primary KPI and one safety KPI. Common pairs: conversion rate with refund rate as safety, deflection rate with CSAT as safety, time to first touch with reply rate as safety. Secondary metrics include (AHT) Average Handle Time, reopen rate, show rate, and pipeline created.

We set a clean baseline in week 1, run an A/B or holdout a control group where practical, and track impact weekly. ROI is calculated from either revenue lift, cost saved, or a blended view. Simple example: additional qualified bookings × close rate × average deal value, minus pilot cost.