Most AI projects end as a demo: a notebook, a chatbot, a pilot that never gets permission to touch real data. We build the other kind - agents, generative AI applications and models that work inside your operations, with evaluations, permissions and an audit trail.
20 years of software delivery, 1,500+ businesses, 70+ brands. Model-neutral by design, fixed scope on discovery, and honest about the work AI should not do.
A pilot is typically live in 4 to 6 weeks. Production usually follows in the same quarter.
An AI development company designs, builds and runs AI systems inside your business. That includes the unglamorous parts: finding the data, defining what a correct answer looks like, connecting the model to your systems, controlling what it is allowed to do, and watching it after launch. The model is one component. The delivery around it is the product.
This is why a two-week prototype can look impressive and still fail in month two. The demo answers whether the model can do something. Production answers whether the business can trust it, afford it, and keep it working. If a vendor cannot explain how correctness will be measured before the build starts, the project is a research bet, not a delivery plan. Our AI readiness assessment exists to surface that before anyone signs a build contract.
Every engagement below ships with an evaluation set, a permission model and an owner after launch. The first release is deliberately narrow: one workflow, measured, in production.
Agents that read from your systems, decide within rules, and act with approval. Support triage, finance follow-up, onboarding, research and reporting. More on AI agents for business.
Products and internal tools built on large language models: drafting, summarisation, extraction, search and analysis, with the guardrails that make the output usable.
Your documents, policies, tickets and contracts turned into a grounded answer system with citations, access control and a measurable retrieval quality target.
Churn, demand, pricing, risk, fraud and forecasting models trained on your data, evaluated against a baseline, and deployed with drift monitoring.
Document and invoice extraction, quality inspection, shelf and inventory recognition, site and safety checks, and image search.
Voice agents and assistants for inbound calls, booking, qualification and support, with transcription, turn-taking, escalation and a clean handover to a person.
Add AI to software you already run: summarise in the CRM, classify in the helpdesk, extract in the ERP, without replacing the system your team knows.
A new product where AI is the core, not a feature. Scoped as a testable MVP with a real user, a real workflow and a defined measure of success before a full build.
An AI system is only as good as its understanding of the work around it. These are the sectors where we already know the data, the regulations and the failure modes.
KYC review, document extraction, dispute triage, credit memos and fraud signals, built with the audit trail a regulated process needs. Fintech development.
Patient intake, clinical documentation support, coding and claims checks, and scheduling, with PHI handling and human sign-off. Healthcare software.
Lease abstraction, listing enrichment, tenant triage, valuation support and portfolio reporting. Real estate software.
Guest messaging, rate and availability agents, review response, itinerary support and OTA operations. Hotel software.
Document processing, exception triage, ETA prediction, carrier matching and customs paperwork. Logistics software.
Catalogue enrichment, product Q and A, returns triage, demand forecasting and merchandising support. Ecommerce software.
Admissions triage, learning content support, assessment feedback and student services, as delivered for GEMS Education.
AI features inside your own product: summarisation, search, agents, extraction and evaluation, delivered with your team. Custom development.
A pilot that cannot fail is a demo. Each stage has a written exit test, and a failed test ends or reshapes the project before the next invoice.
One to two weeks. We inspect the process, the data and the systems, then rank use cases by value, feasibility and risk. Deliverable: a shortlist with effort, cost band and a go or no-go recommendation.
One to three weeks. We profile the data, fix access, define ownership, and decide what can be used and what must be excluded. If the data cannot support the use case, we say so here rather than after the build.
One to two weeks. We build the test set before the model: real inputs, expected outputs, edge cases and the failure that matters most. This becomes the acceptance test for everything that follows.
Four to six weeks. One workflow, real users, real data, limited permissions. Success is measured against the evaluation set and a business metric, not a feeling in a demo.
Four to eight weeks after a passing pilot. Integration, permissions, approvals, audit logging, fallbacks, cost controls and operational runbooks.
Ongoing. Quality sampling, drift and cost monitoring, model and prompt updates, and a monthly review of what the system got wrong and what changed.
Published bands, not "contact us for pricing". The range is wide because the work is not one thing. These are the numbers we quote against, and what moves you up or down inside them.
| Engagement | Typical range | Time | What is included |
|---|---|---|---|
| Discovery and AI audit | $6,000 - $15,000 | 1 - 2 weeks | Credited against the build if you proceed |
| Pilot and proof of value | $15,000 - $35,000 | 4 - 6 weeks | One workflow, real data, evaluation set included |
| Single AI feature in production | $60,000 - $180,000 | 8 - 16 weeks | Integration, permissions, evals, monitoring |
| Agent platform, several workflows | $150,000 - $500,000 | 4 - 9 months | Multi-agent, approvals, audit, connectors, support |
| Monitoring and improvement | $2,000 - $8,000 / month | Ongoing | Quality sampling, drift, model updates, incidents |
What moves the number. The count of systems to integrate, how clean and accessible the data is, the accuracy bar you need (a 95% extraction target and a 99.5% one are different projects), whether the work is regulated, and whether the model has to run in your own environment.
What we fix before we start. Discovery scope, pilot scope, and the acceptance criteria. Change the criteria mid-build and it becomes a new phase with a new price, which is fairer than a surprise at the end.
What stays visible. Model and infrastructure usage is a separate line, so you can see what the system costs to run as well as what it costs to build. A system that is cheap to build and expensive to run is not cheap.
AI projects fail quietly when the output is a demo and a slide. These are the artefacts we commit to, written into the statement of work, not implied.
No black-box handover. If you decided to run the system with your own team tomorrow, you would have everything needed to do it: the code, the prompts, the test set, the results, the documentation and the logs. That is also the test of whether a vendor built you a product or rented you an outcome.
Not every problem needs a custom build. The right answer depends on whether the work is a product, a process, or a role. Use this table before you use a proposal.
| Option | Best when | Time to value | What you control | Main risk |
|---|---|---|---|---|
| Custom build with us | The workflow is core, the data is yours, no product fits | 8 - 20 weeks | Full: code, prompts, data, infrastructure | Delivery risk if scope stays vague |
| Buy an AI SaaS tool | The need is standard and a good product already exists | Days | Limited: their roadmap, their pricing | Lock-in and per-seat cost as you grow |
| Hire in-house | You have a platform team and permanent AI work | 3 - 6 months to hire and ramp | Full | Hiring time, single point of failure |
| AI employee on Brahma | The work is a repeatable role, not a new product | Days to 4 weeks | Your workflows and data; we run the platform | Less suited to novel model research |
A repeatable role - inbound triage, finance follow-up, reporting, onboarding - is usually better as an AI employee than as a custom build, because the platform already exists. A new product, a proprietary model, or a workflow that is your competitive advantage is usually better as a custom build. If a SaaS tool fits, we will say so and point you at the category. The platform behind the AI employee option is Brahma, and the two can run together.
A good AI partner saves you from the wrong project as often as it wins the right one. These are the cases where we will tell you not to build.
If two experienced people disagree and cannot write the rule down, an agent will learn the disagreement. Fix the process first.
Synthetic data can prototype an interface. It cannot prove the system will work on your real inputs.
If the task runs twice a month, the build will never pay back. Buy a tool or leave it alone.
Use deterministic software. A probabilistic system is the wrong instrument for an absolute rule.
A model without an owner degrades quietly. If no one can review the failures monthly, do not deploy it.
Automating a two-dollar task with a five-dollar inference bill is a hobby. We price the run before we build.
Model choice is a procurement decision, not a religion. We route each task to the model that meets the quality bar at the lowest cost, and we design so the model can be replaced without rebuilding the product.
OpenAI, Anthropic, Google, Meta, Qwen, DeepSeek and Mistral, hosted or open-weight, chosen per task. A routing layer lets us switch provider or fall back when quality, price or availability changes.
Agent frameworks, tool calling, queues, retries, state and human checkpoints. The orchestration is where reliability lives, not in the prompt.
Postgres with pgvector, Qdrant, OpenSearch, Snowflake or BigQuery, with chunking, metadata filters and permissions-aware retrieval so the model only sees what the user is allowed to see.
Golden sets, LLM-as-judge with human review, regression tests in CI, and red teaming against the failures that matter: wrong extraction, invented policy, missed escalation.
Input and output filters, PII redaction, token and cost limits, traces, audit logs and alerts on quality or spend. Every run is inspectable.
Cloud, private VPC, on-premises or hybrid. Containers, infrastructure as code, secret management, backups and a rollback path that has been tested.
An AI system with access to your systems needs a stronger baseline than a website. These are the controls we design into every build, not an enterprise add-on.
The rule is simple: the model gets the minimum access the task needs, irreversible actions wait for a person, and every decision is logged.
We use the data the task needs, redact what it does not, and can keep processing in a region you choose, including your own environment.
Per-tool scopes, role-based access for your team, and approval gates on anything that leaves the business or changes a record.
Correctness is tested before launch and re-tested after every model or prompt change. Known failure modes are documented, not discovered in production.
No lock-in to a single provider. If a model is deprecated, too expensive, or outgrown, we switch it behind the interface.
You own the code, the prompts, the data and the model artefacts we build for you. Full transfer on delivery, with the documentation to run it.
A named contact, a severity path, and a written process for model failure, data exposure or cost runaway. Tested before go-live.
Six builds with a named client and a scope you can check. Read the case study before the call; it is a better use of the first ten minutes.
Reservation, inventory and operations systems for a chain operating at scale. The kind of booking flow where a wrong availability answer costs a real room.
$3.1B raised Housing financeCustomer-facing platforms and internal systems for a large lending business, including the workflows behind a high-volume sales operation.
$2.4B revenue business Multi-school education groupTeachMeNow learning platform and school operations software, delivered across a multi-school group with different local requirements.
Multi-school rollout Peer-to-peer RV rentalsA marketplace on BistroStays: listings, availability, booking, payments and payouts, with the owner and renter journeys in one product.
Web, Android and iOS Recruitment platformA recruiting platform on nLance and Jobgator: job matching, pipelines, employer workflows and candidate communication.
Two-product platform Government tourism OTAA national tourism booking platform with inventory, payments and reporting for a government-backed operator.
Public-sector OTADelivery record: 20+ years, 1,500+ businesses served, 70+ brands, 500+ products shipped. Reviews: ITProfiles 4.2/5 from 431 reviews and Trustpilot 4.4/5 from 94 reviews.
No bait-and-switch team. The people in the proposal are the people on the build, and you meet the AI lead in the first call.
Owns the use case, the evaluation set and the technical decisions. One accountable person from audit to production.
Model selection, fine-tuning, retrieval, evaluation and the pipeline that keeps quality measurable.
Access, pipelines, quality, lineage and the permissions that decide what the model can see.
The product around the model: interface, integrations, approvals, audit and the operational tooling.
Builds the test set, runs regression and red-team passes, and signs off the release against the acceptance criteria.
Scope, plan, risks, stakeholder communication and the monthly review of what the system got wrong.
Ask these before you sign. The answers matter more than the deck, and a good vendor will welcome most of them.
The most common failure is not a weak model. It is a project with no definition of correct, no data owner and no plan for the day after launch.
Use these to sanity-check a proposal. If a quote is far below the range, something is missing from the scope; if it is far above, ask what is being built that you did not ask for.
| Project type | First release | Typical range | Good fit for |
|---|---|---|---|
| RAG knowledge assistant | 6 - 12 weeks | $40,000 - $120,000 | Document-heavy support, policy, legal or operations search |
| Single AI feature in an existing product | 8 - 16 weeks | $60,000 - $180,000 | Extraction, classification, drafting or summarisation inside your app |
| Agent workflow with approvals | 10 - 20 weeks | $90,000 - $250,000 | Multi-step work across two or more systems, with a human gate |
| Voice agent | 6 - 12 weeks | $50,000 - $150,000 | Inbound calls, booking, qualification, escalation |
| Computer vision inspection | 10 - 20 weeks | $70,000 - $200,000 | Documents, quality control, shelf or site checks |
| AI-native MVP | 6 - 10 weeks | $35,000 - $90,000 | A new product with AI at the core, scoped to a testable release |
| Agent platform, several workflows | 4 - 9 months | $150,000 - $500,000 | Multiple agents, shared memory, audit, connectors and support |
It designs, builds and runs AI systems inside a business. That covers the use case and data audit, the evaluation set that defines correct output, the model and retrieval layer, integration with your systems, permissions and approvals, and monitoring after launch. The model is one part of the delivery, not the whole job.
A discovery and audit is typically $6,000 to $15,000. A pilot is $15,000 to $35,000. A single AI feature in production usually lands between $60,000 and $180,000, and a multi-workflow agent platform between $150,000 and $500,000. The range is driven by integrations, data quality, the accuracy bar and regulation, not by the model licence.
A pilot is normally live in 4 to 6 weeks. A production feature takes 8 to 16 weeks after that, depending on integration and approvals. A multi-workflow platform takes 4 to 9 months. We do not promise a two-week production system; what can be done in two weeks is a prototype.
Yes, under a data processing agreement, with the access controls and residency you need. We use only the data the task requires, redact what it does not, and your data is not used to train models. If the data cannot support the use case, the audit will say so before a build starts.
Usually both, in the right order. Most business use cases start with prompting and retrieval over your own content, because it is faster and easier to correct. Fine-tuning is added when the evaluation set shows a real gain in format, tone, domain vocabulary, latency or cost. The decision is made on measured results, not preference.
You own the application code, the prompts, the data and the model artefacts we build for you, with full transfer on delivery. Third-party foundation models remain under their own licences, which we document. There is no lock-in clause and no hostage code.
No honest vendor can guarantee a percentage before seeing your data. What we do guarantee is the process: an agreed metric, a test set built from your real inputs, measured results before launch, documented failure modes, and a fix path. If the measured result does not clear the bar, the pilot does not go to production.
Yes. Llama, Qwen, DeepSeek, Mistral and other open-weight models are part of the standard menu, including on-premises deployment when data cannot leave your environment. We compare them against hosted models on your evaluation set, including cost per task.
Ground the model in retrieved sources, require citations, constrain output with structured schemas, validate against rules, and route uncertain or high-impact answers to a person. Then measure it: hallucination is a quality metric with a test set, not a promise.
Yes. We start with a short audit: what was built, what the data supports, what is missing, and whether to finish it or rebuild. Sometimes the right answer is to stop. The audit is fixed price and you keep the findings either way.
Yes. Our engineers can embed in your team on a monthly basis, with the same vetting and the same delivery standards. It suits teams that have a platform and need AI capacity, rather than a full outsourced build.
Yes. We deploy to your cloud account, a private VPC, on-premises or a hybrid, depending on where the data is allowed to go. You can bring your own model keys or use ours, and the deployment is documented so your team can operate it.
The engineering discipline is the same, but three things change. The output is probabilistic, so correctness has to be measured rather than assumed. Data quality becomes a first-class dependency. And the system needs monitoring after launch because models, prompts and real-world inputs drift. Those three differences are why evaluation and observability are in every scope we write.
A 30-minute scoping call, then a fixed-price audit if the use case looks real. You leave the call with a view on feasibility, the data you will need, a cost band and the first release we would recommend. No obligation and no generic deck.
A repeatable role handled by an agent on Brahma, from $99 a month.
Automation that survives real-world input, with approvals and a log.
Take an existing pilot to production: integration, grounding, evals, governance.
The bespoke platform work that often sits beside an AI build.
Real clients, real projects. Nadia Almedro, founder of the travel company Safari Finders, on working with us.
Nadia Almedro, Founder
Safari Finders
Dan Otieno, Founder
Client video testimonial
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