AI & Automation
Put AI to work on the processes that actually cost you time
Applied AI engineering — assistants, agents, document workflows and automation — built on your data with evaluation and guardrails from day one.
The problem
What usually brings people here
If two or more of these describe your situation, they are probably the same underlying problem.
Prototypes that never reach production
A demo built in a notebook behaves differently once it meets real users, real permissions and real edge cases. Without evaluation, monitoring and a rollback path, teams stall at the pilot stage.
No reliable way to measure quality
Teams ship AI features without a test set, so nobody can say whether a prompt change made things better or worse. Every release becomes a judgement call.
Manual work buried in tools and inboxes
Operations run on copy-paste between a CRM, a spreadsheet and email. The cost is invisible until you count the hours.
Legitimate concerns about data exposure
Legal and security teams block AI work when they cannot see where data goes, what is retained, or who can read a model response.
Our approach
How we run ai & automation work
We build AI features the way we build any production system: with a defined scope, a data contract, an evaluation set and observability from the first sprint. That approach is deliberately unglamorous, and it is the difference between a demo and a feature your customers rely on.
Most engagements start with a narrow, high-value workflow — a support queue, a document review step, an internal search problem — and expand only once the first one measurably works. You get a functioning system early rather than a strategy deck.
We are model-agnostic. Where a hosted frontier model is the right call, we use one; where a smaller fine-tuned or self-hosted model meets the bar at a fraction of the cost, we use that instead, and we design the abstraction so you can change your mind later.
Capabilities
What we actually do
Each capability below is a service you can engage on its own or as part of a broader programme.
AI Consulting
A structured assessment of where AI is worth applying in your business, and where it is not. We map candidate workflows, estimate effort and value, check data readiness, and return a sequenced plan you can fund.
- Opportunity mapping across support, operations, sales and engineering
- Data readiness and access review
- Build-versus-buy analysis for each candidate
- Cost modelling per workflow, including inference and human review
Generative AI and LLM Development
Retrieval pipelines, structured extraction, summarisation and generation features built against your content, with an evaluation harness that turns quality into a number you can track across releases.
- Retrieval-augmented generation with chunking and re-ranking tuned to your corpus
- Structured output with schema validation and repair
- Prompt and model versioning tied to your deployment pipeline
- Offline evaluation sets plus online feedback capture
AI Agent Development
Multi-step agents that call your internal tools and APIs to complete real work — with explicit permission boundaries, deterministic fallbacks and a full audit trail of every action taken.
- Tool and function definitions mapped to existing services
- Scoped credentials and per-action authorisation checks
- Human-in-the-loop approval for irreversible steps
- Traces and replay for every agent run
Enterprise Chatbots
Assistants for customers or staff that answer from your own documentation, respect user permissions, cite sources, and hand over to a person when confidence is low.
- Grounded answers with inline citations
- Row- and document-level permission filtering
- Escalation to live support with full conversation context
- Containment, deflection and satisfaction reporting
Workflow Automation
Automation of the repetitive path between your systems: intake, classification, routing, enrichment, approval and follow-up — with retries, idempotency and alerting that a business owner can actually read.
- Event-driven pipelines with durable queues and retries
- Document intake, classification and data extraction
- Approval workflows with audit history
- Operational dashboards for volume, failure and exception rates
AI Integration
Adding AI to a product you already run, without a rewrite. We work inside your existing codebase, deployment process and design system so the feature ships as part of your normal release train.
- Model gateway with provider fallback and cost controls
- Caching and batching to keep inference spend predictable
- Feature flags and staged rollout
- Latency, cost and quality telemetry per feature
Use cases
Situations we are asked about most
Support deflection with grounded answers
An assistant answers tier-one questions from your help centre and product docs, cites its sources, and escalates anything it cannot answer confidently — with a dashboard showing what it handled and what it passed on.
Document intake and extraction
Invoices, contracts, claims or CVs arrive as PDFs and email attachments. The pipeline classifies each one, extracts structured fields, flags low-confidence values for human review, and writes clean records into your system of record.
Internal knowledge search
Staff search across wikis, tickets, code and drive documents through one permission-aware interface, so an answer never surfaces a document the person is not allowed to see.
Sales and CRM enrichment
Inbound leads are researched, summarised, scored and routed automatically, so a rep opens a record that is already prepared rather than starting from a name and an email address.
Quality review of high-volume output
An automated reviewer scores outbound content, transcripts or listings against your own rubric and surfaces only the items that need a human eye.
Deliverables
What you get, concretely
Everything below is handed over as part of the engagement, not sold separately afterwards.
- Working AI feature deployed to your environment behind a feature flag
- Evaluation set and scoring harness you own and can extend
- Prompt, model and retrieval configuration under version control
- Cost and latency telemetry per feature, with alerting thresholds
- Guardrail layer: input filtering, output validation, refusal handling
- Runbook covering failure modes, rollback and escalation
- Handover session and written architecture documentation
Delivery process
Discovery and workflow selection
We interview the people doing the work, quantify volume and handling time, and pick the first workflow on value, feasibility and data availability.
- Workflow map
- Value and effort estimate
- Data access plan
Data and evaluation setup
Before any model work, we assemble a representative test set with expected outputs so quality can be measured rather than argued about.
- Evaluation set
- Scoring rubric
- Baseline scores
Prototype against real data
A narrow end-to-end slice built on production-shaped data, reviewed weekly with the people who will use it.
- Working prototype
- Measured baseline
- Cost model
Productionisation
Authentication, permissions, rate limits, caching, retries, observability and the guardrail layer — the work that turns a prototype into a system.
- Deployed service
- Guardrails
- Dashboards and alerts
Staged rollout
Release behind flags to an internal group, then a customer cohort, watching quality and cost telemetry at every step.
- Rollout plan
- Feedback loop
- Go/no-go review
Iterate and expand
Feed real usage back into the evaluation set, tune, and move to the next workflow once the first is measurably holding up.
- Improvement backlog
- Updated evaluation set
- Next-workflow plan
Technology
The stack behind this practice
Selected per engagement. We recommend based on your team and constraints, not on preference.
Models and inference
- Anthropic Claude
- OpenAI
- Google Gemini
- Llama
- Mistral
- Amazon Bedrock
- Azure OpenAI
Orchestration
- TypeScript SDKs
- Python
- LangGraph
- Temporal
- Celery
- Inngest
Retrieval and data
- pgvector
- Pinecone
- Qdrant
- Elasticsearch
- Redis
Evaluation and observability
- Custom eval harnesses
- OpenTelemetry
- Langfuse
- Grafana
- Sentry
Runtime
- AWS
- Google Cloud
- Azure
- Vercel
- Kubernetes
- Docker
Security & quality
Non-negotiables on every engagement
- Data flow mapping before implementation: what leaves your perimeter, to whom, and under what retention terms
- Provider configuration reviewed for training opt-out and data residency options
- Secrets held in a managed secret store, never in prompts, code or client bundles
- Permission filtering applied at retrieval time so responses cannot cross access boundaries
- Prompt-injection mitigations: input sanitisation, tool allow-lists, and output validation before any side effect
- Full audit logging of agent actions with replay for incident review
Related work
A worked example
An illustrative engagement showing how this practice runs end to end.
- HRTechSample content
An applicant tracking platform with explainable candidate matching
A recruitment platform where structured CV parsing and explainable matching cut screening time without removing recruiter judgement.
- Next.js
- TypeScript
- Node.js
- PostgreSQL
Add AI to Your Product
A production AI feature inside your existing codebase — with evaluation, guardrails, cost control and observability included.
See the approachAutomate Business Workflows
Event-driven automation across your existing systems — intake, classification, routing, approval and reporting, with exception handling that a business owner can operate.
See the approach
Engagement options
How to engage this practice
Dedicated Developers
A team that needs specific skills and already has engineering management in place.
Individual engineers who join your team full time, work in your repository and your process, and report to your leads. You direct the work day to day.
- Managed by
- You
- Commitment
- Monthly, typically three months minimum
Team Extension
Scaling an existing team quickly while keeping product direction fully in-house.
A group of engineers integrated into your existing team structure. Your leads set priorities and run the process; we handle recruitment, retention, performance and continuity.
- Managed by
- You, with our engineering support behind the team
- Commitment
- Monthly, typically three months minimum
Managed Delivery Pods
Owning an outcome end to end when you do not have management capacity to spare.
A cross-functional pod — engineers, QA, design and a delivery lead — that takes a defined scope and runs it. You set priorities and review outcomes; we run the delivery.
- Managed by
- Xalicon
- Commitment
- Quarterly, aligned to a defined scope
Offshore Development Centre
Building a durable long-term engineering capability outside your home market.
A dedicated long-term team operating as your extended engineering function, with its own hiring plan, career development and delivery structure aligned to your organisation.
- Managed by
- Shared governance between your leadership and ours
- Commitment
- Annual, with a defined growth plan
Questions
AI & Automation — questions we are asked
AI & Automation
Start a ai & automation engagement
Tell us the outcome you need. We will tell you what it takes, what it does not, and where we would push back.
Prefer email? contact@xalicon.co