Customer login ↗︎Let’s talk ↗︎

AURTIV SERVICES / 14 SPECIALIST CAPABILITIES

From a useful idea to an AI system at work.

Connect the right interface, knowledge, actions and operating controls. Start with one task and add capability when the evidence supports it.

Let’s shape the next step ↗︎Explore the details ↓
THOUGHTFULLY CONNECTED.BUILT AROUND YOUR WORK.Discover more ↓

A connected engagement

Choose a starting point.
Connect what comes next.

Connect the right interface, knowledge, actions and operating controls. Start with one task and add capability when the evidence supports it. The groups below show complementary capabilities. Choose a focused assignment or combine them into a scoped delivery plan.

01 / CONNECTED CAPABILITIES

Understand & create

Applied AI / 04

Generative AI Development

LLM-powered applications for drafting, summarisation, analysis and business-specific assistance.

Let’s discuss this capability ↗︎

Turn a blank page into a useful first draft.

Generative AI can help people create, summarise and transform content within an existing workflow. The product work lies in supplying the right context, presenting useful controls and making review straightforward. We focus on a defined audience and content type, with an agreed standard for accuracy, tone and completeness rather than treating fluent output as a finished result.

IN PRACTICE

Draft a customer proposal

Illustrative engagement

01 / THE STARTING POINT

The challenge

Sales specialists repeatedly assemble first drafts from approved service descriptions and a customer brief.

02 / THE WORK

Our approach

Build a structured drafting flow that asks for missing requirements, uses approved material and separates supplied facts from assumptions. Let reviewers edit each section and retain the final approved version.

03 / THE DIFFERENCE

How it helps

A reusable starting point can reduce repetitive composition while preserving expert judgement. Track editing effort and factual corrections, not simply the number of generated words.

Explore delivery guidance & success measures

Build it thoughtfully

  • Keep approved reference material versioned and distinguish it from user-provided instructions.
  • Design review and revision into the interface, including a clear way to reject an unsuitable draft.

Know what good looks like

  • Draft usefulness: reviewer acceptance and the extent of required rewriting.
  • Content quality: unsupported statements, omitted requirements and consistency with the brief.

Can generated content be published automatically?

That depends on the use case and agreed controls. Begin with reviewed drafts when factual accuracy, customer commitments or brand language matter; automate publication only after a separate acceptance decision.

Applied AI / 05

RAG Development

Retrieval-augmented assistants connected to approved documents and enterprise knowledge.

Let’s discuss this capability ↗︎

Make company knowledge easier to find and verify.

Retrieval-augmented generation connects a question to relevant source material before composing an answer. The work includes preparing documents, respecting access rules, retrieving useful passages and showing where the answer came from. It is particularly relevant when people need answers from changing internal knowledge rather than a model’s general training.

IN PRACTICE

Find the current operating procedure

Illustrative engagement

01 / THE STARTING POINT

The challenge

Employees struggle to distinguish current instructions from old policy documents and duplicated knowledge-base pages.

02 / THE WORK

Our approach

Index an approved collection with owner, version and access metadata. Retrieve relevant passages, display source links and dates, and explain when the collection does not contain enough evidence to answer.

03 / THE DIFFERENCE

How it helps

Employees can reach supporting material more directly. Evaluate whether the right source was found separately from whether the final answer represented it correctly.

Explore delivery guidance & success measures

Build it thoughtfully

  • Preserve document permissions through ingestion and retrieval, including deletion and access changes.
  • Test ambiguous questions, conflicting versions and questions with no supported answer.

Know what good looks like

  • Retrieval usefulness: relevant evidence present in the retrieved results.
  • Answer support: claims traceable to the cited passages and appropriate abstention.

Will RAG eliminate incorrect answers?

No. Retrieval can miss the right passage, and generation can misinterpret it. Source visibility, targeted evaluation and a way to decline unsupported questions remain part of the design.

Applied AI / 06

AI Chatbot Development

Customer-facing and internal assistants with scoped knowledge and a clear handoff to people.

Let’s discuss this capability ↗︎

Help visitors finish a task, not just exchange messages.

A useful chatbot has a defined service boundary: who it serves, which questions it can answer and when it should involve a person. We design conversation flows alongside the knowledge and business integrations, including identity checks where needed. The interface should make supported actions clear and let users recover easily when an answer is unhelpful.

IN PRACTICE

Guide an authenticated support enquiry

Illustrative engagement

01 / THE STARTING POINT

The challenge

Customers need help understanding an order or choosing the right support route.

02 / THE WORK

Our approach

Combine approved answers with authorised order lookup, collect only the information needed for the case and transfer unresolved conversations with a concise summary. Keep sensitive changes behind the normal verification process.

03 / THE DIFFERENCE

How it helps

Customers may reach the right answer with fewer repeated explanations. Judge success by resolved needs and useful handoffs rather than conversation length or containment alone.

Explore delivery guidance & success measures

Build it thoughtfully

  • Offer a visible route to human help and pass the conversation context with the handoff.
  • Test confusing phrasing, repeated questions and unavailable integrations as normal user journeys.

Know what good looks like

  • Resolution: confirmed completion of the user’s intended task.
  • Handoff quality: repeated information, avoidable transfers and reviewer-rated summaries.

Can the chatbot connect to our helpdesk?

Integration depends on your helpdesk APIs, permissions and identity model. Discovery identifies which operations can be supported and what users should see when the integration is unavailable.

Applied AI / 07

AI Voice Agents

Voice assistants for enquiries, bookings and lead qualification with consent and escalation controls.

Let’s discuss this capability ↗︎

Make spoken interactions clear and recoverable.

Voice agents combine speech recognition, conversation logic and speech output with telephony or an application. Their quality depends on more than the words: pauses, interruptions, noisy audio and transfer behaviour all affect the experience. We scope a narrow call purpose and design a recovery path for unclear information or a caller who needs a person.

IN PRACTICE

Handle appointment enquiries

Illustrative engagement

01 / THE STARTING POINT

The challenge

A service desk answers repeated availability questions while staff also manage complex customer needs.

02 / THE WORK

Our approach

Connect approved scheduling operations, repeat critical details for confirmation and allow callers to interrupt or request a transfer. Design a fallback when audio is unclear or the scheduling system cannot respond.

03 / THE DIFFERENCE

How it helps

Routine enquiries can be handled more consistently while staff retain ownership of exceptions. Measure successful bookings and caller effort alongside response delay.

Explore delivery guidance & success measures

Build it thoughtfully

  • Confirm names, dates and other critical details before completing the action.
  • Agree disclosure, recording, retention and escalation requirements with the business before launch.

Know what good looks like

  • Conversation completion: confirmed outcomes, abandonment and transfer reasons.
  • Voice experience: response delay, misunderstood turns and successful interruptions.

What happens when the agent cannot understand a caller?

Use a limited clarification sequence, then offer an alternative route or transfer. Repeatedly asking the same question should not trap a caller in an automated loop.

02 / CONNECTED CAPABILITIES

Act & coordinate

Applied AI / 01

AI Agent Development

Custom agents for a defined business task, with approved tools, access boundaries and human escalation.

Let’s discuss this capability ↗︎

Give a recurring task a dependable owner.

An agent combines a model with tools and a defined task. The useful starting point is a piece of work with a recognisable finish: preparing an account brief, checking an incoming request or assembling evidence for a reviewer. We map the decisions, permitted actions and exception routes before building the agent so its behaviour can be assessed against the actual job.

IN PRACTICE

Prepare an account briefing

Illustrative engagement

01 / THE STARTING POINT

The challenge

An account team spends its preparation time moving between CRM records, support history and approved product documents.

02 / THE WORK

Our approach

Connect read-only sources, identify the account, assemble a dated briefing and attach evidence for each material statement. Missing records go into a review queue rather than being filled with guesses.

03 / THE DIFFERENCE

How it helps

The team can spend more of its meeting preparation on decisions. Compare research time and reviewer corrections with the existing process before widening access.

Explore delivery guidance & success measures

Build it thoughtfully

  • Make each tool permission explicit; separate reading information from changing records.
  • Set completion criteria and a retry budget so an unsuccessful task ends with an understandable handoff.

Know what good looks like

  • Task acceptance: proportion of completed briefings approved without material correction.
  • Human effort: preparation plus review time, including escalated and failed tasks.

Does an agent need unrestricted access?

No. Start with the smallest set of tools needed for the assignment. A draft-only or read-only agent can demonstrate usefulness before any write action is considered.

Applied AI / 02

Agentic AI Solutions

Multi-step automation that can plan, use tools and pause for approval at agreed decision points.

Let’s discuss this capability ↗︎

Coordinate work that changes as it unfolds.

Some workflows cannot be captured as a single prompt or fixed checklist. An agentic solution can choose a next step using the evidence available, while the surrounding application controls permissions, budgets and approvals. We first identify where this flexibility is useful and where a predictable rule is preferable, then design a workflow that people can inspect and interrupt.

IN PRACTICE

Resolve an incomplete service request

Illustrative engagement

01 / THE STARTING POINT

The challenge

Incoming requests contain different combinations of documents, missing fields and follow-up questions.

02 / THE WORK

Our approach

Classify the request, identify missing evidence, draft a targeted follow-up and present a proposed resolution. Keep approvals and any irreversible action in a separate, explicit step.

03 / THE DIFFERENCE

How it helps

Reviewers receive a coherent work package instead of scattered messages. Measure the whole resolution cycle, including requests that need manual intervention.

Explore delivery guidance & success measures

Build it thoughtfully

  • Use fixed steps for predictable rules and model-directed steps only where interpretation is needed.
  • Persist task state so interruptions, approvals and retries do not restart completed work.

Know what good looks like

  • Resolution quality: accepted outcomes by request type and difficulty.
  • Coordination effort: handoffs, repeated questions and elapsed time per completed request.

When is a simpler workflow better?

When the inputs, decisions and outputs are stable, a conventional automation may be easier to maintain. Discovery should justify each agentic step rather than assuming more autonomy creates more value.

Applied AI / 03

Multi-Agent Systems

Collaborating agents with distinct responsibilities for research, coordination and task execution.

Let’s discuss this capability ↗︎

Divide complex work without losing accountability.

A multi-agent system assigns different responsibilities to cooperating components. It can be useful when research, checking and synthesis have genuinely different tools or evaluation needs. The design challenge is coordination: deciding who owns the result, what information moves between roles and how conflicting conclusions are resolved. We make those boundaries visible before adding more agents.

IN PRACTICE

Build a reviewed research pack

Illustrative engagement

01 / THE STARTING POINT

The challenge

A team needs source collection, factual checks and an executive summary for the same question.

02 / THE WORK

Our approach

Give a researcher access to approved sources, a reviewer a checking rubric and a coordinator responsibility for the final pack. Require evidence to survive every handoff and return unresolved disagreements to a person.

03 / THE DIFFERENCE

How it helps

Specialisation may make complex work easier to review, but adds coordination cost. Compare the design with a single-agent baseline before choosing the larger system.

Explore delivery guidance & success measures

Build it thoughtfully

  • Define typed handoff records rather than passing unconstrained conversation histories.
  • Set a shared time and cost budget, with a clear owner for conflicts and incomplete tasks.

Know what good looks like

  • Coordination quality: missing evidence, contradictory findings and repeated work.
  • Incremental value: accepted-result quality and cost against the simpler baseline.

Does adding more agents improve accuracy?

Not automatically. Extra agents can repeat the same mistake or amplify it. Use independent checks and evaluate the complete system, including failures at the handoffs.

Applied AI / 08

AI Workflow Automation

Automate repetitive work across documents, approvals and existing business applications.

Let’s discuss this capability ↗︎

Remove repetitive handoffs from a real process.

Workflow automation connects triggers, decisions and actions across business systems. AI is useful for interpreting variable inputs, while ordinary rules often remain the best way to validate fields or route an approved record. We map the end-to-end process, including exceptions and ownership, so automation reduces work instead of creating a new queue of unexplained failures.

IN PRACTICE

Prepare incoming invoices for review

Illustrative engagement

01 / THE STARTING POINT

The challenge

A finance operations team rekeys document details and chases missing purchase references.

02 / THE WORK

Our approach

Extract candidate fields, validate them against approved records and route uncertain matches to a reviewer. Record the decision and prevent retries from creating duplicate downstream entries.

03 / THE DIFFERENCE

How it helps

The intended benefit is less rekeying and a clearer exception queue. Compare processing effort across both straightforward and problematic documents.

Explore delivery guidance & success measures

Build it thoughtfully

  • Make actions safe to retry and keep an audit trail of each change.
  • Separate interpretation from approval so low-confidence extraction does not silently become a business decision.

Know what good looks like

  • Processing effort: touch time and manual interventions per completed item.
  • Exception control: duplicate actions, unresolved cases and age of the review queue.

Must the whole process be automated at once?

No. A first increment can prepare records or draft actions for approval. This exposes real exceptions before additional steps are connected.

Applied AI / 14

AI Integration Services

Connect AI capabilities to existing CRM, ERP, helpdesk and internal applications through approved interfaces.

Let’s discuss this capability ↗︎

Connect intelligence to the systems where work happens.

AI integration connects an existing model or assistant to business applications, identities and data flows. The difficult part is often maintaining the rules of the surrounding system: who can see a record, which changes are allowed and what happens when a dependency fails. We design the integration as a dependable application boundary.

IN PRACTICE

Add assisted triage to an existing helpdesk

Illustrative engagement

01 / THE STARTING POINT

The challenge

A support team wants suggestions in its current workflow without introducing a separate workspace.

02 / THE WORK

Our approach

Read permitted ticket context, generate a category and draft response, and place the suggestions in the helpdesk for review. Track failures and prevent repeated events from duplicating notes or actions.

03 / THE DIFFERENCE

How it helps

People can use AI support inside a familiar process. Assess whether suggestions reduce effort after accounting for review, corrections and integration maintenance.

Explore delivery guidance & success measures

Build it thoughtfully

  • Reuse the application’s identity and authorisation rules rather than granting a shared broad account.
  • Define timeout, retry and fallback behaviour for every external dependency.

Know what good looks like

  • Workflow adoption: suggestions used and the editing needed before approval.
  • Integration health: failed events, duplicate writes and time to recover delayed work.

Do we need to replace our existing software?

Not necessarily. Available APIs, extension points and permissions determine whether the capability can be added to your current application or needs a separate interface.

03 / CONNECTED CAPABILITIES

Evaluate & operate

Applied AI / 09

LLM Fine-Tuning

Adapt suitable models to domain tasks when evaluation shows a need beyond prompting or retrieval.

Let’s discuss this capability ↗︎

Teach a repeatable behaviour with suitable examples.

Fine-tuning adapts a model using examples of the behaviour you want. It may help with a specialised format, classification task or consistent style when prompting alone is insufficient. We begin by defining the failure to fix, checking the training examples and comparing alternatives, because fine-tuning is an investment in data preparation and continuing evaluation.

IN PRACTICE

Standardise a specialised extraction format

Illustrative engagement

01 / THE STARTING POINT

The challenge

A team needs consistently structured output from a recurring document type, but prompt variations still produce avoidable format errors.

02 / THE WORK

Our approach

Curate representative input-output pairs, separate evaluation cases and compare the adapted model with the existing prompt-based baseline. Inspect rare categories and invalid outputs before considering deployment.

03 / THE DIFFERENCE

How it helps

The value is more dependable task behaviour, subject to evidence. Include dataset maintenance, inference costs and review effort in the comparison.

Explore delivery guidance & success measures

Build it thoughtfully

  • Remove duplicates and keep evaluation examples independent from training decisions.
  • Document dataset provenance, permitted use and the versions used for each experiment.

Know what good looks like

  • Task quality: schema validity and field-level correctness on held-out examples.
  • Operational fit: latency and cost per accepted output compared with the baseline.

Is fine-tuning the best way to add current company knowledge?

Not usually as a first choice. Frequently changing facts often belong in a retrieval system. Fine-tuning is better evaluated for a defined behavioural change with suitable examples.

Applied AI / 10

AI Model Deployment

Deploy AI models into a client-selected cloud or private environment with defined operating controls.

Let’s discuss this capability ↗︎

Move a validated model into a controlled service.

A model that works in an experiment still needs an operating environment. Deployment covers packaging, interfaces, capacity, security and release controls, as well as the way the application responds when inference fails. We design around expected traffic and the consequences of a slow or unavailable prediction, with a documented path back to a known version.

IN PRACTICE

Serve a model inside a business application

Illustrative engagement

01 / THE STARTING POINT

The challenge

An internal prototype needs to support application requests without depending on a developer’s workstation.

02 / THE WORK

Our approach

Package the model and preprocessing, define a versioned API, test representative traffic and introduce health checks. Release to a limited audience with fallback behaviour and an agreed rollback trigger.

03 / THE DIFFERENCE

How it helps

The application gains a repeatable release process and clearer ownership. Evaluate service behaviour under realistic demand before expanding usage.

Explore delivery guidance & success measures

Build it thoughtfully

  • Version the model, preprocessing and configuration together so results can be reproduced.
  • Test cold starts, capacity limits and dependency failures rather than only successful predictions.

Know what good looks like

  • Serving reliability: successful requests and response-time distribution at expected load.
  • Release safety: time to detect a regression and restore the approved version.

Can deployment use our existing cloud?

Usually this is assessed first. Runtime compatibility, networking, hardware needs, data residency and operational ownership determine the appropriate hosting option.

Applied AI / 11

AI Architecture Consulting

Design enterprise AI systems around your data, integrations, security boundaries and operating budget.

Let’s discuss this capability ↗︎

Choose the smallest architecture that meets the need.

AI architecture consulting turns a proposed use case into explicit technical decisions. We examine how information enters the system, where models add value, which actions require oversight and how the capability will be operated. The result should help your team choose between practical options and understand their costs, limitations and dependencies.

IN PRACTICE

Choose an enterprise assistant architecture

Illustrative engagement

01 / THE STARTING POINT

The challenge

A business is deciding between a hosted assistant, a custom retrieval system and an agent connected to internal tools.

02 / THE WORK

Our approach

Map users and data boundaries, compare options against representative tasks and document tradeoffs in integration, control and ongoing maintenance. Use a focused prototype to investigate the most uncertain assumption.

03 / THE DIFFERENCE

How it helps

Decision makers receive a reasoned design and an implementation sequence instead of an unqualified technology recommendation.

Explore delivery guidance & success measures

Build it thoughtfully

  • Record assumptions and the evidence that would change an architecture decision.
  • Treat evaluation, access control and operating ownership as architecture components from the start.

Know what good looks like

  • Decision readiness: unresolved dependencies and evidence available for the chosen option.
  • Feasibility: quality, latency and operating cost demonstrated in the bounded prototype.

Do you recommend one model provider for every project?

No. The choice depends on task performance, deployment constraints, data handling, commercial terms and integration needs. A comparison should use your representative workload.

Applied AI / 12

AI Agent Testing & Evaluation

Assess answer quality, reliability, permission boundaries and tool-action behaviour before expansion.

Let’s discuss this capability ↗︎

Understand where an agent works—and where it stops.

Agent evaluation examines the path to an outcome as well as the final response. A plausible answer can hide an incorrect tool call, a missed approval or unnecessary repetition. We create a test programme around real tasks, difficult exceptions and the actions that matter most to your business, then turn the findings into release decisions.

IN PRACTICE

Review an agent before enabling write actions

Illustrative engagement

01 / THE STARTING POINT

The challenge

A team has a working assistant and wants to let it update selected business records.

02 / THE WORK

Our approach

Build cases covering authorised updates, ambiguous requests, denied access and failed tools. Inspect the proposed actions and resulting records, with separate release gates for each permission level.

03 / THE DIFFERENCE

How it helps

The team can see which capabilities are ready and which need revision. A benchmark becomes a continuing regression check rather than a one-off demonstration.

Explore delivery guidance & success measures

Build it thoughtfully

  • Include unsuccessful tasks and adversarial inputs, not only curated demonstrations.
  • Use human-reviewed criteria for consequential outcomes and calibrate any automated judging.

Know what good looks like

  • Behavioural quality: task success and compliance with the agreed action boundaries.
  • Regression exposure: new failures by tool, permission level and task category.

Can one overall score tell us whether to release?

It is rarely sufficient. Break results down by task and failure severity so strong performance on easy cases cannot hide an unacceptable error in a critical action.

Applied AI / 13

MLOps & LLMOps

Build repeatable model and prompt lifecycles from development through deployment and operation.

Let’s discuss this capability ↗︎

Make model changes repeatable and observable.

MLOps and LLMOps connect experimentation to the everyday operation of an AI system. The work includes versioning, validation, release controls and monitoring across models, data and, where relevant, prompts and retrieval configuration. We focus on the changes your team actually makes and the evidence needed to move each change into use.

IN PRACTICE

Release prompt and model updates safely

Illustrative engagement

01 / THE STARTING POINT

The challenge

A team regularly adjusts an assistant but cannot easily explain why quality changed between versions.

02 / THE WORK

Our approach

Version the relevant artefacts, run a representative evaluation set in the release pipeline and retain the configuration behind each result. Add a limited rollout and a documented way to restore a prior version.

03 / THE DIFFERENCE

How it helps

Teams can investigate regressions with a consistent record of what changed. Measure the operational burden as well as the speed of releases.

Explore delivery guidance & success measures

Build it thoughtfully

  • Keep evaluation datasets and acceptance decisions traceable to each release.
  • Separate monitoring signals from retraining or rollout actions; do not automatically promote every new version.

Know what good looks like

  • Change reliability: regressions introduced, detected and reversed per release.
  • Operational effort: manual release steps and time spent reproducing an incident.

Is this only useful for large model teams?

No. A small team can start with version control, a repeatable evaluation run and a release checklist. Automation can grow as the change volume and operating needs increase.

HOW THE WORK CONNECTS

A practical path through the practice.

  1. 01

    Frame one task and identify the sources, users and permissions.

  2. 02

    Build a bounded workflow and test representative successes and exceptions.

  3. 03

    Release with agreed monitoring, review and ownership before expanding.

SCOPE & COMMERCIALS

Combine capabilities.
Keep responsibilities clear.

The practice is a way to plan related work together. It does not require purchasing every service or changing your existing subscription.

Start with a bounded assessment, a defined project or an agreed allocation of specialist capacity. Pricing depends on the scope, dependencies and delivery model.

Confirm third-party costs, access, handover and ongoing support before work begins.

Discuss this practice ↗︎

RESEARCH & FURTHER READING

Explore the thinking behind the work.

Anthropic · Building effective agents ↗︎

A reference for choosing between predictable workflows and model-directed agents.

Microsoft · Retrieval-augmented generation ↗︎

An introduction to grounding answers in retrieved source material.

Further reading behind the practice. These resources do not imply vendor affiliation or certification. Engagement examples describe possible work, not customer results.