AI that does real work, with a human still in charge
Most teams have already tried a chatbot and found it does not know their business. We build AI systems grounded in your own documents and data (answering, extracting, classifying and routing) with evaluation, guardrails and human review designed in from the start. Where a rules-based automation is the better answer, we say so.
- Retrieval-augmented generation over your own documents and systems
- Structured extraction, classification and routing at production quality
- Agent workflows with human-in-the-loop, evaluation sets and guardrails
Who this is for
Organisations with a real, repetitive, language-heavy workload and the data to ground it, not those looking for a demo.
Operations and back-office teams
Your people read documents, answer the same questions and re-key data between systems all day. The knowledge exists; it lives in PDFs, email threads and a shared drive nobody searches.
Regulated and data-sensitive businesses
Finance, healthcare, legal and public-sector-adjacent firms in the US, UAE and Pakistan that cannot send customer data to a public API and need private or self-hosted models with an audit trail.
Product teams adding AI features
You have a working product and want AI in it (search, summarisation, assistants) without a rewrite, without runaway inference costs and without shipping something that confidently makes things up.
What we build
Each of these is a pattern we have put into production. Most engagements start with one and add another once the first is measurably working.
Retrieval-augmented generation (RAG)
Question answering and assistants grounded in your own documents, tickets, wikis and databases rather than the model's general knowledge. This is the right pattern when the answers already exist somewhere in your organisation and the problem is finding and applying them. We handle ingestion, chunking, hybrid search, citations and access control so users only see what they are allowed to see.
- Document ingestion and incremental re-indexing
- Hybrid vector and keyword retrieval with reranking
- Cited answers with links back to source
- Permission-aware retrieval per user and role
Structured data extraction
Turning invoices, contracts, KYC documents, claims forms, emails and scanned PDFs into validated, typed records your systems can use. It applies wherever people currently read a document and type what they find into another screen. Outputs are schema-checked, confidence-scored and routed to a person when the model is unsure.
- Schema-constrained JSON output
- OCR and layout handling for scans
- Field-level confidence and validation rules
- Exception queue for human review
Classification and routing
Support tickets, inbound email, transactions, leads and content sorted into the right category, priority and owner. Useful when volume is high, categories are known, and mistakes are cheap to correct but expensive to leave unhandled. We benchmark against a labelled sample first so you know the accuracy before it goes live.
- Ticket triage and priority scoring
- Intent detection for chat and email
- Fraud and anomaly flagging for review
- Accuracy measured on your own labelled data
Agent workflows with human-in-the-loop
Multi-step automations where the model plans, calls your tools and APIs, and hands off to a person at defined checkpoints, drafting a response, preparing a reconciliation, assembling an onboarding pack. Appropriate when the task is a sequence of judgement calls rather than a single answer. Every action is logged, reversible where possible, and gated when it touches money or customer records.
- Tool and API calling against your systems
- Approval steps before irreversible actions
- Full audit log of every step and decision
- Fallback to a human on low confidence
Evaluation, guardrails and monitoring
The part most AI projects skip. We build an evaluation set from your real cases before writing prompts, measure every change against it, and add guardrails for prompt injection, off-topic requests, PII leakage and unsafe output. In production, quality, cost and latency are tracked so regressions are seen before your customers see them.
- Golden evaluation sets from real cases
- Regression testing on every prompt or model change
- Input and output guardrails
- Cost, latency and quality dashboards
Custom models, fine-tuning and private hosting
Classical machine learning for forecasting, scoring and recommendations where a language model is the wrong tool; fine-tuning only when prompting and retrieval have measurably hit their limit; and open-weight models deployed in your own cloud or on-premises when data cannot leave your control.
- Forecasting, scoring and recommendation models
- Fine-tuning with a clear before-and-after evaluation
- Self-hosted open-weight models on your infrastructure
- Model and provider swapping without a rewrite
Also part of the engagement
Models, frameworks & infrastructure
Data pipelines
Cleaning, joining and refreshing the data the system depends on. Most AI quality problems turn out to be data problems.
Integration with your systems
CRM, ERP, ticketing, email and internal APIs connected so the AI acts on live data and writes back where it should.
Security and data governance
Encryption, access control, retention rules and provider data-processing terms reviewed against your obligations.
Cost and latency engineering
Model routing, caching, batching and prompt trimming so the bill and the response time stay predictable at volume.
How we approach AI work
Automation first, AI where it earns its place
If a rule, a regex or a plain integration solves it, we build that. Language models go where the input is genuinely unstructured or the judgement is genuinely fuzzy.
Measured before it is trusted
An evaluation set from your real cases exists before the first prompt is written. Nothing ships on the strength of a good-looking demo.
Your data, your models, your accounts
Vector stores, fine-tuned weights, prompts and pipelines live in your infrastructure under your API keys. Switching providers later is a configuration change, not a rebuild.
What the engagement looks like
- 01
Discovery and feasibility
We look at the actual workload, sample the data, and tell you honestly whether AI is the right tool and what accuracy is realistic.
- 02
Evaluation set and prototype
A labelled set of real cases, then a narrow prototype measured against it. You see numbers, not a demo, before committing to a build.
- 03
Build and harden
Integration, guardrails, human review paths, cost controls and monitoring. Rolled out to a small group first, then widened.
- 04
Operate and improve
Quality, drift, cost and latency watched in production. Evaluation sets grow as edge cases appear; a retainer covers model and provider changes.
What you receive at the end
Everything runs in accounts you control from the first commit. Ending an engagement is an access change, not a migration.
- Source code, prompts and pipelines in your repository
- Evaluation set and accuracy report
- Vector stores and models in your accounts
- Guardrail and human-review configuration
- Cost, latency and quality dashboards
- Handover session and runbooks for your team
We’ve got the answers you seek
Anything not covered here? Ask us through the contact form and we’ll answer within a working day.
It depends on the provider and the contract tier, and we review this with you before any data moves. Enterprise and API tiers from the major providers generally exclude your data from training under their data-processing terms; we confirm the specific terms in writing. Where that is not acceptable (regulated data, contractual restrictions, or in-country residency requirements in the UAE or Pakistan) we deploy open-weight models inside your own cloud or on-premises so nothing leaves your control.
Tell us which workflow you want to automate
What the input looks like, who handles it today and what a wrong answer would cost. We'll come back within 24 hours with an honest view of whether AI fits, what accuracy is realistic and what a first phase would involve.
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