Research
Being established
The applied research programme on how AI behaves inside real workflows.
Output that proves it
A public evaluation method with its limits.
Funded from a fixed monthly budget, reviewed each month.
Research →Advanced AI systems and applied research
Most enterprise AI is still stuck between demonstration and production.
Chainfore engineers bounded AI workflows for the agent-native enterprise. Each one has a defined boundary, verified results, recovery paths and evidence an auditor can inspect.
Chainfore Private Limited · Lahore, Pakistan · Runs inside your identity, data and controls
Engineering over demonstration.
Systems must run inside the customer's identity, data and operating processes, not beside them.
Evidence over adjectives.
Each performance statement comes with its method, baseline, limits and owner.
Autonomy within boundaries.
Systems act within explicit permissions. Consequential actions stay under human approval.
One company, four units.
Each has one job and one output that proves it.
Systems
In development
The assurance and reliability layer the delivery work uses: evaluation harnesses, permission checks, recovery patterns and release checks.
Output that proves it
A reusable release-assurance toolkit, used across engagements.
Built from delivery work, not sold as a standalone product.
Systems →Enterprise
Commercial delivery
The delivery business. Enterprises buy five offers from this unit, from a two-week workflow baseline to a dedicated engineering team.
Output that proves it
Paid, measured engagements with written acceptance criteria.
Every engagement has a named technical lead and delivery lead.
Enterprise →Ventures
Owned products and a gated pipeline
Chainfore's own products: consumer products already live, and an enterprise pipeline that opens only when a product passes its own acceptance tests.
Output that proves it
A product that has passed its own acceptance tests and is in use.
Live: Mehram. In development: Falconic, Wajib, Wisaal.
Ventures →Enterprise, and five offers.
Start with the offer that fits the problem. Each has a named buyer, a written output, a stated duration and a boundary agreed before work begins.
01Workflow architecture and baselineDecide whether AI belongs in a workflow, and what a measurable first version looks like.Two to three weeks.02Assured workflow engineeringBuild one integrated workflow to a stated boundary, with the controls and evidence to run it.Six to ten weeks.03Agent release assuranceTest an existing agent before it is expanded, and produce evidence a release decision can rely on.Four to six weeks.04Reliability operationsKeep a live workflow dependable as models, tools, data and volumes change.Monthly.05Dedicated engineering deliveryA Chainfore-managed team owning a defined workstream in your programme.Three to twenty-four months.
Past work, shown with permission.
Each story shows its method and its limits, and is published only with the written agreement of the organisation that holds the relationship.
Client stories are published as permissions are confirmed. Until then, the method we hold every story to is on How you can check us.
How you can check us →Numbers only when they are measured.
We publish a result with its method, dataset, baseline, repeat count and limits. Until then, this site shows the standard we hold ourselves to, not a claim.
Benchmarks and standard →Start with one workflow.
Tell us the process, the cost of failure and the systems involved. We will respond with a proposed first step.
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