Knowledge assistant
Answers questions from your policies, contracts and wikis with citations, replacing hours of searching across drives and inboxes.
What we measure Time to find an answer
AI & automation solutions
AI solutions built to work in production.
Four solution patterns, from knowledge assistants to workflow automation. Each one starts with a short pilot so you can judge real results on your own data before you scale.
Solution patterns
What we build, and what it replaces.
Four patterns we build. Each one targets a specific manual process, with a measurable result to check against.
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Document processing
Reads invoices, claims and contracts, extracts the fields that matter and routes only the exceptions to a person.
What we measure Share processed untouched -
Support copilot
Drafts replies, summarises long threads and suggests the next best action, while the agent stays in control of what is sent.
What we measure Time to first reply -
Workflow automation
Agents that finish multi-step back-office tasks across your tools, with approval gates wherever a human should decide.
What we measure Hours saved per week
AI IN MOTION
See what production AI can look like.
Four interactive examples of the AI we build: chat, agents, vision and code. Switch between them.
GENERATIVE AI · RAG
AI copilots grounded on your data.
RAG-based assistants that answer accurately from your private knowledge base — never hallucinating, always citing sources, always cost-controlled.
- Hybrid search · semantic + keyword reranking
- Source-cited answers · auditable & explainable
- Multi-tenant · per-user permissions
- Multilingual · streaming responses
Aries Chat · Demo
DEMOU · User
What's our refund policy for enterprise contracts?AI · Copilot
Per contract clause 7.3, enterprise contracts have a 14-day notice refund window for pre-paid annual commitments. Pro-rated refunds apply for usage-based components.Source: MSA-v4.2.pdf p.12
U · User
Can it be extended for strategic accounts?...Looking up policies for strategic accounts...
⚡ 184ms📚 4 sources retrieved🎯 0.94 confidence
Example
An assistant that cites its sources.
Ask a question in plain language. The assistant searches your policies, contracts and tickets, answers in seconds and shows the paragraph it used, so people can verify instead of trust.
- Respects the permissions of each user
- Works with PDFs, wikis, email and SharePoint
- Quality tracked against a test set of real questions
What is our refund window for annual plans?
Annual plans can be refunded in full within 30 days of purchase; after that, refunds are pro-rated per remaining full month.
Source · Billing policy v4.2, section 3.1
Retrieval 0.911.4 s3 sources
Delivery use cases
Four problems we solve again and again.
Each starts with the problem as teams commonly describe it, then shows how we approach it, what we would measure and how long it typically takes.
Modernisation
Move off the legacy system without stopping the business.
The problem
A decade-old monolith holds business logic nobody fully documented. Every release is risky, hosting costs keep climbing and the one engineer who understands it is close to retirement.
Timeline
4 to 9 months
Phased by module, with a production release every 3 to 4 weeks
Typical stack
- .NET
- Java
- Node.js
- Docker
- Kubernetes
- AWS
- Azure
- Terraform
- PostgreSQL
Our approach
- Map the system with traffic analysis and code inspection to find seams that can be cut safely
- Replace one module at a time behind an API gateway, so old and new run side by side
- Migrate data in rehearsed batches with automated reconciliation before every cutover
- Add automated tests and CI/CD first, so each later release gets cheaper and calmer
What we would measure
- compared before and after
- Hosting cost
- releases per month
- Release pace
- planned cutover window
- Downtime
AI integration
Put language models to work inside your own product.
The problem
The team has a demo that answers five hand-picked questions well, then falls apart on real traffic. It leaks private data, costs more than expected and nobody can say whether it is getting better.
Timeline
4 weeks to pilot, 3 months to scale
A working pilot on your data first, then hardening and rollout
Typical stack
- Python
- OpenAI
- Azure OpenAI
- Anthropic
- LangChain
- pgvector
- Pinecone
- FastAPI
Our approach
- Choose the smallest model that clears the quality bar, then add retrieval over your own documents
- Build an evaluation set from real questions and run it on every change before release
- Enforce per-user permissions, redact personal data and keep an audit log of every answer
- Track cost and latency per request, with fallbacks and human review for low-confidence answers
What we would measure
- before and after
- Lookup time
- scored on a test set
- Answer quality
- tracked through the pilot
- Cost per query
Mobile launch
Take an app from idea to the store in one quarter.
The problem
You have a validated idea and a launch window but no mobile team. Hiring takes longer than the window, and agencies that hand over a prototype leave you to discover why it will not pass store review.
Timeline
10 to 14 weeks
Design sprint, three build sprints, then beta and store submission
Typical stack
- React Native
- Flutter
- Swift
- Kotlin
- Firebase
- Node.js
- Fastlane
- Sentry
Our approach
- Run a two-week design sprint to agree the core journey and cut everything that can wait for version two
- Build one cross-platform codebase, with native modules only where performance genuinely needs them
- Ship weekly builds to a private beta so real users shape the product before launch
- Handle store submission, analytics, crash reporting and push notifications as part of the project
What we would measure
- kickoff to release
- Time to store
- sessions at launch
- Crash-free
- first quarter after launch
- Store rating
Data platforms
Get one version of the truth that people actually trust.
The problem
Finance, sales and operations each report a different revenue number. Dashboards come from spreadsheets exported by hand, and no one can say where a figure came from or when it was last refreshed.
Timeline
8 to 16 weeks
First trusted dashboard in about 6 weeks, then source-by-source expansion
Typical stack
- Snowflake
- BigQuery
- Databricks
- dbt
- Airflow
- Fivetran
- Power BI
- Looker
Our approach
- Inventory the sources and agree the ten business definitions that must match everywhere
- Land raw data in a warehouse or lakehouse, then model it in version-controlled, tested transformations
- Add freshness checks and lineage, so every chart can show where its numbers came from
- Hand over a semantic layer and a small set of dashboards the business owns and can extend
What we would measure
- how recent each number is
- Freshness
- of revenue across teams
- One definition
- spent on manual reporting
- Analyst time
Industries
Domain knowledge where mistakes are costly.
We build for regulated and high-stakes sectors, so audit trails and privacy requirements are designed in, not bolted on.
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Healthcare
Consent-aware telehealth, imaging triage and clinical note summaries
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Fintech and banking
Fraud scoring, lending decisions and regulatory reporting
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Ecommerce
Semantic search, personalised ranking and returns prediction
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Logistics
Route planning, fleet telemetry and demand-aware capacity
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Education
Adaptive practice, tutoring assistants and progress analytics
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Real estate
Automated valuation, lead scoring and listing enrichment
How we implement
From idea to measured result.
We treat AI like any other product: a defined problem, a baseline, a pilot and a number that tells you whether it worked.
- 01
Score the use cases
1 week
Rank ideas by value, data readiness and risk, so you know which are worth building and which are not.
- 02
Four-week pilot
4 weeks
A working prototype on your real data, with a success metric agreed before we write code.
- 03
Harden and integrate
6 to 10 weeks
Security review, evaluation sets, monitoring and integration with the systems your team already uses.
- 04
Operate and improve
Ongoing
Cost tracking, drift alerts and a quarterly improvement cycle after launch, measured against the baseline.
Tools we commonly use
- Python
- OpenAI
- Azure OpenAI
- Anthropic
- LangChain
- pgvector
- Pinecone
- PyTorch
- Databricks
- AWS
- Senior engineers
- 4
- Hands-on experience each
- 3+ years
- Remote delivery, any country
- Global
- Typical first reply
- 1 day
Questions
What teams ask before they start.
Is our data used to train public models?
No. We use enterprise endpoints that do not train on your inputs, and we can deploy inside your own cloud account or on private infrastructure. Personal data is redacted before it reaches a model where possible, access follows each user's existing permissions, and every request is logged for audit.
What does an AI pilot cost?
A short pilot is a fixed fee, agreed after a free scoping call, so you know the ceiling before we start. Running costs after launch depend on usage, and we report cost per request throughout the pilot so the production estimate rests on real numbers rather than guesses.
How accurate will it be, and how do you measure it?
We agree what good looks like and build a test set from your own real cases before development starts. Every change is scored against that set, and low-confidence answers are routed to a person. You see the scores each week, so there are no surprises at handover.
Who owns the models, code and prompts?
You do. Source code, prompts, evaluation sets, fine-tuned weights and documentation are transferred to you, under a written assignment of intellectual property. We reuse general techniques across clients, but never your data or your business logic.