Language models, agents, and document pipelines wired into the systems you already run, built in whichever technology fits the problem.
Most companies asking about AI automation services do not need a strategy deck. They need one repetitive chunk of work to stop consuming a person's week: the invoices somebody retypes, the inbox somebody triages, the policy question five colleagues ask daily. Language models make that tractable, and they also make it easy to build something nobody can trust in production. We build the trustworthy version: scoped narrowly, measured honestly, and connected to the systems where the work lives.
AI earns its place when the work is high volume, messy, and currently done by people reading things. If one of the following sounds familiar, there is a project worth scoping.
If your bottleneck is a broken process rather than a slow one, start with business process optimisation. Automating a bad process only produces bad outcomes faster, and we will say so.
Language models embedded in your product with structured output, validation, caching, and a defined decision boundary.
Agents given narrow tools, real permissions, and clear stopping conditions, so every action stays reviewable.
Classification and field extraction from invoices, contracts, and claims, validated against your records, doubtful cases sent to review.
Retrieval pipelines over your documents with hybrid search, reranking, permission aware retrieval, and verifiable citations.
Assistants inside the tools people already use, scoped to a domain they serve well, with graceful handover past that.
Triage, routing, summarisation, and drafting joined to your CRM, ticketing, and finance systems, so one event updates everything else.
Forecasting, scoring, anomaly detection, and recommendation built with conventional models where they beat a language model.
Fine tuning and prompt optimisation where a general model falls short, chosen only after retrieval and prompting are tried first.
Labelled test sets from your real inputs, regression runs on every change, and monitoring after launch, so quality stays measured.
A short engagement mapping candidate workflows, checking data availability, and saying plainly which ideas are worth building.
An API key is not an integration. The gap between a convincing demo and something you can put in front of customers is unglamorous engineering: prompt versioning, output that validates, retries that do not double charge anyone, and a boundary around what the model decides alone.
Agent development follows the same discipline. An agent is a model given tools and a loop, so the interesting questions are about permissions and stopping conditions, not prompts. We scope tools narrowly and keep a person in the loop wherever an action is hard to reverse.
Schema constrained responses checked before they touch a database, so a malformed answer fails loudly rather than writing nonsense into your records.
The model reads and writes through defined functions with real permissions, turning a chat window into something that completes work.
Confidence thresholds, refusal handling, and approval on anything irreversible, reviewed against the OWASP Top 10 for LLM Applications. Automation should escalate the unusual case, not guess.
A labelled test set built from your real inputs, so a model change can be proven better rather than assumed better.
Document work tends to pay back fastest, because volume is high and accuracy is measurable against records you hold. Invoices, contracts, claims, and applications all follow the same pipeline shape. Before scoping one, it is worth working through the questions that separate a process worth automating from one that will just fail faster.
Document automation is a confidence dial, not a switch. A pipeline handling most volume cleanly and handing the rest to a person beats one claiming to handle everything.
Retrieval augmented generation is how an assistant answers from your documents instead of inventing something plausible. Most of the work, and most failures, is retrieval.
Parsing so structure survives, then splitting at sensible boundaries. Tables and headings change answer quality more than model choice.
Semantic search alone misses exact part numbers and policy codes. Adding keyword search and reranking is usually the biggest quality jump.
An assistant must never answer from a document the person asking could not open. Access control is enforced at retrieval time.
Every answer links to its source, and the index refreshes as documents change rather than ageing into wrong answers.
The model is one step in a chain, and value appears when that step joins the tools your team already opens every morning.
Messages, tickets, and applications classified by intent and urgency, then assigned and drafted against, so the first human touch is a decision.
Call notes and long threads condensed into the format your team uses, on a schedule, delivered where they already look.
Your CRM, ERP, ticketing, accounting, and storage connected through APIs, queues, and webhooks.
A legacy system with no API and a screen level answer sits with business process optimisation. Compute or a deployment pipeline sits with DevOps and infrastructure. An application built around the model is custom software development, and the outcome level view is our automate your operations solution.
This section exists because almost nobody publishes it. These are the cases where we decline, or where we insist a person stays in the loop. The framing follows the NIST AI Risk Management Framework, released January 2023.
Being specific about limits is how a project survives real volume, and why scoping usually shrinks a brief rather than growing one. Deciding where AI belongs across a whole estate is IT consulting and technology strategy work.
There is no single best model. A supplier naming one before hearing your requirements is selling a partnership, not making an engineering decision. Four things settle it.
Drafting a reply and extracting a payment amount sit at opposite ends of the tolerance scale, and the acceptable error rate decides how much review you need.
Contractual, regulatory, and residency constraints can rule out hosted providers, which is why we keep open weight options in regular use.
A thousand documents a night and a reply in two seconds are different problems. Batch tolerance changes the model tier and architecture.
We keep the provider behind an interface so a better or cheaper model swaps in without a rewrite. This field moves too fast to marry a vendor.
| Consideration | Hosted model behind an API | Open weight model we run for you | Classical ML or plain code |
|---|---|---|---|
| Where your data goes | To a third party, under that provider's terms | Only to infrastructure you control | Nowhere it does not already go |
| Handling of messy free text | Strongest available today | Strong, with more tuning effort | Poor, unless the input is structured |
| Repeatability of the answer | Varies, and can change when the provider updates | Fixed while you pin the weights | Identical every time |
| Operational burden on you | Lowest, an API key and a budget | Highest, you own the serving stack | Low, it is ordinary software |
| Where we usually recommend it | Drafting, summarising, and classification at moderate volume | Regulated data, residency limits, or very high volume | Arithmetic, reconciliation, and anything deterministic |
Generative systems carry risks older machine learning does not, and we work from the NIST Generative AI Profile (NIST AI 600-1) when deciding which of the three a workflow should use.
AI automation platforms where the model does the work rather than decorating the interface.
We map candidate workflows against real examples and volume, then estimate achievable accuracy. Some ideas get ruled out here, which is the point.
We agree the metric before building: extraction accuracy, deflection rate, or hours returned. An unmeasurable goal produces an unfinishable project.
A working pipeline runs against a labelled sample of your data, so you see real performance and failure modes, not a curated demo.
Connection to your systems, permissions, validation, review queues, logging, and cost controls, where most engineering time honestly goes.
A staged rollout with monitoring and regression tests, so drift and provider changes are caught by the system, not your customers.
Breadth is what makes an honest recommendation possible. These are the tools we use regularly, grouped by the part of the system they serve.
One workflow proven in production beats a platform nobody adopts. If an existing product would do the job, that is the recommendation.
We agree what accuracy must look like on your data, then measure it. Nothing goes live on a good looking demo alone.
Prompts, code, pipelines, evaluation sets, and accounts are yours. The provider sits behind an interface, so you can move when the market moves. See how engagements are scoped and ended.
The same team builds the application, the integrations, and the data layer, because an AI feature is only as good as the software carrying it.
It depends on the workflow, so we quote after a short scoping conversation rather than from a price list. The drivers are volume, how many systems must be connected, the accuracy demanded, and whether an evaluation set has to be built. We will tell you when the payback does not justify it.
A single focused workflow, such as extracting fields from one document type and posting them into an existing system, usually reaches a measurable pilot within a few weeks. Assistants over a large knowledge base run longer, because retrieval quality, permissions, and evaluation take the time.
Not by us, and not by providers unless you choose an arrangement that permits it. Enterprise and API tiers exclude your inputs from training by default, and we confirm that in writing per provider. Where data cannot leave your environment, we build with open weight models on infrastructure you control.
High volume reading, classifying, extracting, summarising, drafting, and searching are realistic today, handling most cases with the rest escalated to a person. Not realistic: final judgement carrying legal or financial consequence, work demanding an identical answer every time, and tasks where nobody can define a correct answer.
It will, so the system is designed around that. Outputs are validated against your own records, low confidence cases route to a review queue, every decision is logged with its inputs, and anything irreversible waits for human approval. Accuracy is monitored after launch, not assumed.
You do. Source code, prompts, pipelines, evaluation sets, any fine tuned weights, and the provider accounts are yours from the start. We take no revenue share and add no proprietary layer, so another team could pick the work up if you wanted.
Tell us which task costs your team the most hours. We will come back with what AI can take, what it cannot, and how we would prove it.
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